NYTimes:Blogs:: Popular classification:: txtfeat
bdanalytics
Date: (Thu) May 14, 2015
Introduction:
Data: Source: Training: https://kaggle2.blob.core.windows.net/competitions-data/kaggle/4347/NYTimesBlogTrain.csv New: https://kaggle2.blob.core.windows.net/competitions-data/kaggle/4347/NYTimesBlogTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
# Gather all package requirements here
#suppressPackageStartupMessages(require())
#packageVersion("snow")
#require(sos); findFn("pinv", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://kaggle2.blob.core.windows.net/competitions-data/kaggle/4347/NYTimesBlogTrain.csv"
glb_newdt_url <- "https://kaggle2.blob.core.windows.net/competitions-data/kaggle/4347/NYTimesBlogTest.csv"
glb_out_pfx <- "NYTBlogs_txtfeat_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newent_dataset <- TRUE # or TRUE
glb_split_entity_newent_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- "<col_name> <condition_operator> <value>" # or NULL
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_drop_vars <- c(NULL) # or c("<col_name>")
#glb_max_fitent_obs <- 2238 # NULL # or any integer
glb_max_fitent_obs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- TRUE; glb_is_binomial <- TRUE
glb_rsp_var_raw <- "Popular"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Popular.fctr"
# if the response factor is based on numbers e.g (0/1 vs. "A"/"B"),
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
relevel(factor(ifelse(raw == 1, "Y", "N")), as.factor(c("Y", "N")), ref="N")
#as.factor(paste0("B", raw))
#as.factor(raw)
}
glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0))## [1] Y Y N N N
## Levels: N Yglb_map_rsp_var_to_raw <- function(var) {
as.numeric(var) - 1
#as.numeric(var)
#levels(var)[as.numeric(var)]
#c(" <=50K", " >50K")[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0)))## [1] 1 1 0 0 0if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# NewsDesk = the New York Times desk that produced the story
# SectionName = the section the article appeared in (Opinion, Arts, Technology, etc.)
# SubsectionName = the subsection the article appeared in (Education, Small Business, Room for Debate, etc.)
# Headline = the title of the article
# Snippet = a small portion of the article text
# Abstract = a summary of the blog article, written by the New York Times
# WordCount = the number of words in the article
# created WordCount.log
# PubDate = the publication date, in the format "Year-Month-Day Hour:Minute:Second"
glb_date_vars <- c("PubDate")
# UniqueID = a unique identifier for each article
glb_id_vars <- c("UniqueID")
glb_is_textual <- TRUE # vs. glb_is_numerical ???
#Sys.setlocale("LC_ALL", "C") # For english
glb_txt_vars <- c("Headline", "Snippet", "Abstract")
glb_append_stop_words <- list() # NULL # or c("<freq_word>")
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitent_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBent_df))
# numrows(glb_OOBent_df) = 1.1 * numrows(glb_newent_df)
#glb_sprs_thresholds <- c(0.982, 0.965, 0.965)
glb_sprs_thresholds <- c(0.982, 0.970, 0.970)
names(glb_sprs_thresholds) <- glb_txt_vars
# List transformed vars
glb_exclude_vars_as_features <- c(NULL) # or c("<var_name>")
if (glb_is_textual)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
# List output vars (useful during testing in console)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# grep(glb_rsp_var_out, names(glb_trnent_df), value=TRUE))
glb_impute_na_data <- TRUE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_models_lst <- list(); glb_models_df <- data.frame()
# rpart: .rnorm messes with the models badly
# caret creates dummy vars for factor feats which messes up the tuning
# - better to feed as.numeric(<feat>.fctr) to caret
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=2, max=4, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL # or "<model_id_prefix>.<model_method>"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 11.135 NA NAStep 1.0: import data
glb_trnent_df <- myimport_data(url=glb_trnng_url, comment="glb_trnent_df",
force_header=TRUE)## [1] "Reading file ./data/NYTimesBlogTrain.csv..."
## [1] "dimensions of data in ./data/NYTimesBlogTrain.csv: 6,532 rows x 10 cols"
## NewsDesk SectionName SubsectionName
## 1 Business Crosswords/Games
## 2 Culture Arts
## 3 Business Business Day Dealbook
## 4 Business Business Day Dealbook
## 5 Science Health
## 6 Science Health
## Headline
## 1 More School Daze
## 2 New 96-Page Murakami Work Coming in December
## 3 Public Pension Funds Stay Mum on Corporate Expats
## 4 Boot Camp for Bankers
## 5 Of Little Help to Older Knees
## 6 A Benefit of Legal Marijuana
## Snippet
## 1 A puzzle from Ethan Cooper that reminds me that a bill is due.
## 2 The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His Years of Pilgrimage.
## 3 Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little about the strategy, which could hurt the nations tax base.
## 4 As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service members ideal customers.
## 5 Middle-aged and older patients are unlikely to benefit in the long term from surgery to repair tears in the meniscus, pads of cartilage in the knee, a new review of studies has found.
## 6 A new study has found evidence that legal access to marijuana is associated with fewer opioid overdose deaths, but researchers said their findings should not be used as the basis for the wide adoption of legalized cannabis.
## Abstract
## 1 A puzzle from Ethan Cooper that reminds me that a bill is due.
## 2 The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His Years of Pilgrimage.
## 3 Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little about the strategy, which could hurt the nations tax base.
## 4 As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service members ideal customers.
## 5 Middle-aged and older patients are unlikely to benefit in the long term from surgery to repair tears in the meniscus, pads of cartilage in the knee, a new review of studies has found.
## 6 A new study has found evidence that legal access to marijuana is associated with fewer opioid overdose deaths, but researchers said their findings should not be used as the basis for the wide adoption of legalized cannabis.
## WordCount PubDate Popular UniqueID
## 1 508 2014-09-01 22:00:09 1 1
## 2 285 2014-09-01 21:14:07 0 2
## 3 1211 2014-09-01 21:05:36 0 3
## 4 1405 2014-09-01 20:43:34 1 4
## 5 181 2014-09-01 18:58:51 1 5
## 6 245 2014-09-01 18:52:22 1 6
## NewsDesk SectionName SubsectionName
## 226 Styles
## 995
## 2124 TStyle
## 3326 TStyle
## 4752 Business Technology
## 6462 Foreign
## Headline
## 226 For Tavi Gevinson, Fashion Takes a Back Seat, for Now
## 995 Reconsidering What to Call an Extremist Group
## 2124 Paris Fashion Week: Kenzo Spring/Summer 2015
## 3326 The Portable Blue Bottle
## 4752 Monster Moves to Restore a Faded Job Search Brand
## 6462 1889: Priest Questions the Meridian of Greenwich
## Snippet
## 226 Tavi Gevinson, the teenage fashion star turned Broadway actress, wont be much of a player at New York Fashion Week this season.
## 995 Editors have decided to adjust how The Times refer to an Islamic extremist group that controls territory in Syria and Iraq.
## 2124 Scenes from the Paris Fashion Week photo diary of Nina Westervelt.
## 3326 The coffee purveyor has teamed up with its fellow Bay Area-based company Timbuk2 to create a travel kit.
## 4752 Monster, which revolutionized online job hunting in the 1990s, is trying to reinvent itself for the era of Twitter and Facebook with new products that capitalize on social media.
## 6462 From the International Herald Tribune archives: Priest Questions the Meridian of Greenwich in 1889.
## Abstract
## 226 Tavi Gevinson, the teenage fashion star turned Broadway actress, wont be much of a player at New York Fashion Week this season.
## 995 Editors have decided to adjust how The Times refer to an Islamic extremist group that controls territory in Syria and Iraq.
## 2124 Scenes from the Paris Fashion Week photo diary of Nina Westervelt.
## 3326 The coffee purveyor has teamed up with its fellow Bay Area-based company Timbuk2 to create a travel kit.
## 4752 Monster, which revolutionized online job hunting in the 1990s, is trying to reinvent itself for the era of Twitter and Facebook with new products that capitalize on social media.
## 6462 From the International Herald Tribune archives: Priest Questions the Meridian of Greenwich in 1889.
## WordCount PubDate Popular UniqueID
## 226 459 2014-09-04 16:55:57 0 226
## 995 301 2014-09-15 16:05:13 0 995
## 2124 59 2014-09-28 11:20:02 0 2124
## 3326 248 2014-10-14 14:45:55 0 3326
## 4752 995 2014-11-02 07:00:31 0 4752
## 6462 110 2014-11-27 12:00:34 0 6462
## NewsDesk SectionName SubsectionName
## 6527 Foreign
## 6528 Opinion Room For Debate
## 6529 Foreign
## 6530 TStyle
## 6531 Multimedia
## 6532 Business
## Headline
## 6527 1914: Russians Dominate in East Poland
## 6528 Finding a Secretary of Defense
## 6529 1889: Metropolitan Opera House Reopens in New York
## 6530 The Daily Gift: Picasso Plates for Creative Dining
## 6531 Racing From New York to Barcelona
## 6532 Math Anxiety: Why Hollywood Makes Robots of Alan Turing and Other Geniuses
## Snippet
## 6527 From the International Herald Tribune archives: Russians dominate in East Poland in 1914.
## 6528 If Chuck Hagel isn't the right Pentagon chief to respond to an onslaught of global crises, who is?
## 6529 From the International Herald Tribune archives: The Metropolitan Opera House reopens in New York in 1889.
## 6530 Each day until Christmas, the editors of T share a new holiday gift idea.
## 6531 A sailboat race from New York to Barcelona was the setting for a thrilling and sometimes terrifying video about this challenging sport.
## 6532 The visionary who stares at formulas written on walls or mirrors or better yet, thin air has become a Hollywood trope. So has the depiction of the genius who cant connect with real people.
## Abstract
## 6527 From the International Herald Tribune archives: Russians dominate in East Poland in 1914.
## 6528 If Chuck Hagel isn't the right Pentagon chief to respond to an onslaught of global crises, who is?
## 6529 From the International Herald Tribune archives: The Metropolitan Opera House reopens in New York in 1889.
## 6530 Each day until Christmas, the editors of T share a new holiday gift idea.
## 6531 A sailboat race from New York to Barcelona was the setting for a thrilling and sometimes terrifying video about this challenging sport.
## 6532 The visionary who stares at formulas written on walls or mirrors or better yet, thin air has become a Hollywood trope. So has the depiction of the genius who cant connect with real people.
## WordCount PubDate Popular UniqueID
## 6527 176 2014-11-30 13:48:40 0 6527
## 6528 1597 2014-11-30 13:27:23 0 6528
## 6529 214 2014-11-30 09:44:57 0 6529
## 6530 61 2014-11-30 09:00:43 0 6530
## 6531 441 2014-11-30 09:00:22 0 6531
## 6532 921 2014-11-30 07:00:40 0 6532
## 'data.frame': 6532 obs. of 10 variables:
## $ NewsDesk : chr "Business" "Culture" "Business" "Business" ...
## $ SectionName : chr "Crosswords/Games" "Arts" "Business Day" "Business Day" ...
## $ SubsectionName: chr "" "" "Dealbook" "Dealbook" ...
## $ Headline : chr "More School Daze" "New 96-Page Murakami Work Coming in December" "Public Pension Funds Stay Mum on Corporate Expats" "Boot Camp for Bankers" ...
## $ Snippet : chr "A puzzle from Ethan Cooper that reminds me that a bill is due." "The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His"| __truncated__ "Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little"| __truncated__ "As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service "| __truncated__ ...
## $ Abstract : chr "A puzzle from Ethan Cooper that reminds me that a bill is due." "The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His"| __truncated__ "Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little"| __truncated__ "As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service "| __truncated__ ...
## $ WordCount : int 508 285 1211 1405 181 245 258 893 1077 188 ...
## $ PubDate : chr "2014-09-01 22:00:09" "2014-09-01 21:14:07" "2014-09-01 21:05:36" "2014-09-01 20:43:34" ...
## $ Popular : int 1 0 0 1 1 1 0 1 1 0 ...
## $ UniqueID : int 1 2 3 4 5 6 7 8 9 10 ...
## - attr(*, "comment")= chr "glb_trnent_df"
## NULLif (glb_is_separate_newent_dataset) {
glb_newent_df <- myimport_data(url=glb_newdt_url, comment="glb_newent_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_entity_df <- myrbind_df(glb_trnent_df, glb_newent_df);
comment(glb_entity_df) <- "glb_entity_df"
} else {
glb_entity_df <- glb_trnent_df; comment(glb_entity_df) <- "glb_entity_df"
if (!glb_split_entity_newent_datasets) {
stop("Not implemented yet")
glb_newent_df <- glb_trnent_df[sample(1:nrow(glb_trnent_df),
max(2, nrow(glb_trnent_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newent_df <- do.call("subset",
list(glb_trnent_df, parse(text=glb_split_newdata_condition)))
glb_trnent_df <- do.call("subset",
list(glb_trnent_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnent_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newent_df <- glb_trnent_df[!split, ]
glb_trnent_df <- glb_trnent_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnent_df <- glb_entity_df
comment(glb_trnent_df) <- "glb_trnent_df"
glb_newent_df <- glb_entity_df
comment(glb_newent_df) <- "glb_newent_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newent_df) <- "glb_newent_df"
myprint_df(glb_newent_df)
str(glb_newent_df)
if (glb_split_entity_newent_datasets) {
myprint_df(glb_trnent_df)
str(glb_trnent_df)
}
} ## [1] "Reading file ./data/NYTimesBlogTest.csv..."
## [1] "dimensions of data in ./data/NYTimesBlogTest.csv: 1,870 rows x 9 cols"
## NewsDesk SectionName SubsectionName
## 1 Culture
## 2 Culture Arts
## 3 Business Crosswords/Games
## 4 Business Business Day Dealbook
## 5 Science Health
## 6 Science Health
## Headline
## 1 'Birdman' Tops the Gothams
## 2 'Sleepy Hollow' Recap: A Not-So-Shocking Death
## 3 Drinking Buddy For Falstaff
## 4 Encouraging Public Service, Through Wall Street's 'Revolving Door'
## 5 Therapy Prevents Repeat Suicide Attempts
## 6 Hoping for a Good Death
## Snippet
## 1 The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner.
## 2 In the fall season finale, a question of where the series has many places to go.
## 3 In which Timothy Polin reveals his potty mouth.
## 4 The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than on good public policy.
## 5 Short-term psychotherapy may be an effective way to prevent repeated suicide attempts.
## 6 What I hadnt considered before my fathers heart attack was the precise meaning of not wanting to live hooked up to machines.
## Abstract
## 1 The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner.
## 2 In the fall season finale, a question of where the series has many places to go.
## 3 In which Timothy Polin reveals his potty mouth.
## 4 The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than on good public policy.
## 5 Short-term psychotherapy may be an effective way to prevent repeated suicide attempts.
## 6 What I hadnt considered before my fathers heart attack was the precise meaning of not wanting to live hooked up to machines.
## WordCount PubDate UniqueID
## 1 111 2014-12-01 22:45:24 6533
## 2 558 2014-12-01 22:01:34 6534
## 3 788 2014-12-01 22:00:26 6535
## 4 915 2014-12-01 21:04:13 6536
## 5 213 2014-12-01 19:13:20 6537
## 6 938 2014-12-01 19:05:12 6538
## NewsDesk SectionName SubsectionName
## 3 Business Crosswords/Games
## 725 TStyle
## 731 Business Business Day Dealbook
## 751 TStyle
## 864
## 1376 Business Business Day Small Business
## Headline
## 3 Drinking Buddy For Falstaff
## 725 Ansel Elgort Buttons Up in Brioni
## 731 Didi Dache, a Chinese Ride-Hailing App, Raises $700 Million
## 751 The Daily Gift: A Soft, Colorful Quilt From a Brooklyn Fashion Favorite
## 864 Today in Politics
## 1376 As Health Insurance Evolves, Traditional Brokers Claim They Still Have a Role
## Snippet
## 3 In which Timothy Polin reveals his potty mouth.
## 725 The actor brought a tinge of youthfulness to the classic Italian houses retro-tailored look.
## 731 The Singapore investor Temasek and the Chinese social network operator Tencent are among the leaders of the fund-raising round for a company that says it has 10 times the ridership of Uber.
## 751 Each day until Christmas, the editors of T share a new holiday gift idea.
## 864 The 113th Congress is concluding with partisan brinksmanship and one last mad scramble for votes to pass a $1.1 trillion spending package.
## 1376 Its complex picking insurance for yourself and your family, said a health care policy director for a small-business organization. Its even more complex for a business.
## Abstract
## 3 In which Timothy Polin reveals his potty mouth.
## 725 The actor brought a tinge of youthfulness to the classic Italian houses retro-tailored look.
## 731 The Singapore investor Temasek and the Chinese social network operator Tencent are among the leaders of the fund-raising round for a company that says it has 10 times the ridership of Uber.
## 751 Each day until Christmas, the editors of T share a new holiday gift idea.
## 864 The 113th Congress is concluding with partisan brinksmanship and one last mad scramble for votes to pass a $1.1 trillion spending package.
## 1376 Its complex picking insurance for yourself and your family, said a health care policy director for a small-business organization. Its even more complex for a business.
## WordCount PubDate UniqueID
## 3 788 2014-12-01 22:00:26 6535
## 725 89 2014-12-10 12:30:47 7257
## 731 724 2014-12-10 12:06:32 7263
## 751 85 2014-12-10 09:00:38 7283
## 864 1544 2014-12-11 07:09:25 7396
## 1376 1250 2014-12-18 07:00:05 7908
## NewsDesk SectionName SubsectionName
## 1865
## 1866 Business Technology
## 1867 Metro N.Y. / Region
## 1868 Multimedia
## 1869 Foreign World Asia Pacific
## 1870 Science Health
## Headline
## 1865 Today in Politics
## 1866 Uber Suspends Operations in Spain
## 1867 New York Today: The Year in News
## 1868 New Year, Old Memories, in Times Square
## 1869 Hong Kong Police Criticized After 14-Year-Old's Detention
## 1870 The Super-Short Workout and Other Fitness Trends
## Snippet
## 1865 House Republicans are ending the year on a defensive note over Representative Steve Scalises 2002 speech to a white supremacist group.
## 1866 In a first in the growing pushback against Ubers global expansion, a judges ruling barred telecommunications operators and banks from supporting the companys services.
## 1867 Wednesday: The most read stories of 2014, teeth-chattering cold, and its New Years Eve.
## 1868 What happens when you combine Burning Man, Independence Day fireworks, the last day of school and a full-contact Black Friday sale-a-bration? New Years Eve in Times Square.
## 1869 The authorities have been accused of trying to intimidate young pro-democracy protesters and their families after a 14-year-old girl was detained on suspicion of drawing flowers in chalk near government headquarters and sent to a juvenile home.
## 1870 The big story in exercise science this year was the super-short workout, although many other fitness-related themes emerged in 2014.
## Abstract
## 1865 House Republicans are ending the year on a defensive note over Representative Steve Scalises 2002 speech to a white supremacist group.
## 1866 In a first in the growing pushback against Ubers global expansion, a judges ruling barred telecommunications operators and banks from supporting the companys services.
## 1867 Wednesday: The most read stories of 2014, teeth-chattering cold, and its New Years Eve.
## 1868 What happens when you combine Burning Man, Independence Day fireworks, the last day of school and a full-contact Black Friday sale-a-bration? New Years Eve in Times Square.
## 1869 The authorities have been accused of trying to intimidate young pro-democracy protesters and their families after a 14-year-old girl was detained on suspicion of drawing flowers in chalk near government headquarters and sent to a juvenile home.
## 1870 The big story in exercise science this year was the super-short workout, although many other fitness-related themes emerged in 2014.
## WordCount PubDate UniqueID
## 1865 1616 2014-12-31 07:03:46 8397
## 1866 292 2014-12-31 06:09:32 8398
## 1867 1010 2014-12-31 06:06:58 8399
## 1868 387 2014-12-31 05:00:19 8400
## 1869 717 2014-12-31 04:16:29 8401
## 1870 818 2014-12-31 00:01:10 8402
## 'data.frame': 1870 obs. of 9 variables:
## $ NewsDesk : chr "Culture" "Culture" "Business" "Business" ...
## $ SectionName : chr "" "Arts" "Crosswords/Games" "Business Day" ...
## $ SubsectionName: chr "" "" "" "Dealbook" ...
## $ Headline : chr "'Birdman' Tops the Gothams" "'Sleepy Hollow' Recap: A Not-So-Shocking Death" "Drinking Buddy For Falstaff" "Encouraging Public Service, Through Wall Street's 'Revolving Door'" ...
## $ Snippet : chr "The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner." "In the fall season finale, a question of where the series has many places to go." "In which Timothy Polin reveals his potty mouth." "The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than "| __truncated__ ...
## $ Abstract : chr "The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner." "In the fall season finale, a question of where the series has many places to go." "In which Timothy Polin reveals his potty mouth." "The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than "| __truncated__ ...
## $ WordCount : int 111 558 788 915 213 938 1336 2644 752 99 ...
## $ PubDate : chr "2014-12-01 22:45:24" "2014-12-01 22:01:34" "2014-12-01 22:00:26" "2014-12-01 21:04:13" ...
## $ UniqueID : int 6533 6534 6535 6536 6537 6538 6539 6540 6541 6542 ...
## - attr(*, "comment")= chr "glb_newent_df"
## NULLif (nrow(glb_trnent_df) == nrow(glb_entity_df))
warning("glb_trnent_df same as glb_entity_df")
if (nrow(glb_newent_df) == nrow(glb_entity_df))
warning("glb_newent_df same as glb_entity_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_entity_df <- glb_entity_df[, setdiff(names(glb_entity_df), glb_drop_vars)]
glb_trnent_df <- glb_trnent_df[, setdiff(names(glb_trnent_df), glb_drop_vars)]
glb_newent_df <- glb_newent_df[, setdiff(names(glb_newent_df), glb_drop_vars)]
}
# Check for duplicates in glb_id_vars
if (length(glb_id_vars) == 0) {
warning("using .rownames as identifiers for observations")
glb_entity_df$.rownames <- rownames(glb_entity_df)
glb_id_vars <- ".rownames"
}
if (sum(duplicated(glb_entity_df[, glb_id_vars, FALSE])) > 0)
stop(glb_id_vars, " duplicated in glb_entity_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_vars)
# Combine trnent & newent into glb_entity_df for easier manipulation
glb_trnent_df$.src <- "Train"; glb_newent_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_entity_df <- myrbind_df(glb_trnent_df, glb_newent_df)
comment(glb_entity_df) <- "glb_entity_df"
glb_trnent_df <- glb_newent_df <- NULL
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 11.135 12.185 1.05
## 2 inspect.data 2 0 12.185 NA NAStep 2.0: inspect data
#print(str(glb_entity_df))
#View(glb_entity_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_entity_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
dsp_problem_data <- function(df) {
print(sprintf("numeric data missing in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(names(df), myfind_chr_cols_df(df)),
function(col) sum(is.na(df[, col]))))
print(sprintf("numeric data w/ 0s in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(names(df), myfind_chr_cols_df(df)),
function(col) sum(df[, col] == 0, na.rm=TRUE)))
print(sprintf("numeric data w/ Infs in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(names(df), myfind_chr_cols_df(df)),
function(col) sum(df[, col] == Inf, na.rm=TRUE)))
print(sprintf("numeric data w/ NaNs in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(names(df), myfind_chr_cols_df(df)),
function(col) sum(df[, col] == NaN, na.rm=TRUE)))
print(sprintf("string data missing in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(myfind_chr_cols_df(df), ".src"),
function(col) sum(df[, col] == "")))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnent_df & glb_newent_df
print(myplot_histogram(glb_entity_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_entity_df),
glb_rsp_var, glb_rsp_var_raw))
dsp_problem_data(glb_entity_df)
}
glb_chk_data()## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## Loading required package: reshape2
## Popular.0 Popular.1 Popular.NA
## Test NA NA 1870
## Train 5439 1093 NA
## Popular.0 Popular.1 Popular.NA
## Test NA NA 1
## Train 0.8326699 0.1673301 NA
## [1] "numeric data missing in glb_entity_df: "
## WordCount Popular UniqueID
## 0 1870 0
## [1] "numeric data w/ 0s in glb_entity_df: "
## WordCount Popular UniqueID
## 109 5439 0
## [1] "numeric data w/ Infs in glb_entity_df: "
## WordCount Popular UniqueID
## 0 0 0
## [1] "numeric data w/ NaNs in glb_entity_df: "
## WordCount Popular UniqueID
## 0 0 0
## [1] "string data missing in glb_entity_df: "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate
## 17 0# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_entity_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_entity_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_entity_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## Popular Popular.fctr .n
## 1 0 N 5439
## 2 NA <NA> 1870
## 3 1 Y 1093## Warning: Removed 1 rows containing missing values (position_stack).
## Popular.fctr.N Popular.fctr.Y Popular.fctr.NA
## Test NA NA 1870
## Train 5439 1093 NA
## Popular.fctr.N Popular.fctr.Y Popular.fctr.NA
## Test NA NA 1
## Train 0.8326699 0.1673301 NA# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
myextract_dates_df <- function(df, vars, rsp_var) {
keep_feats <- c(NULL)
for (var in vars) {
dates_df <- data.frame(.date=strptime(df[, var], "%Y-%m-%d %H:%M:%S"))
dates_df[, rsp_var] <- df[, rsp_var]
dates_df[, paste0(var, ".POSIX")] <- dates_df$.date
dates_df[, paste0(var, ".year")] <- as.numeric(format(dates_df$.date, "%Y"))
dates_df[, paste0(var, ".year.fctr")] <- as.factor(format(dates_df$.date, "%Y"))
dates_df[, paste0(var, ".month")] <- as.numeric(format(dates_df$.date, "%m"))
dates_df[, paste0(var, ".month.fctr")] <- as.factor(format(dates_df$.date, "%m"))
dates_df[, paste0(var, ".date")] <- as.numeric(format(dates_df$.date, "%d"))
dates_df[, paste0(var, ".date.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%d")), 5) # by month week
# wkday Sun=0; Mon=1; ...; Sat=6
dates_df[, paste0(var, ".wkday")] <- as.numeric(format(dates_df$.date, "%w"))
dates_df[, paste0(var, ".wkday.fctr")] <- as.factor(format(dates_df$.date, "%w"))
# Federal holidays 1.9., 13.10., 27.11., 25.12.
# NYState holidays 1.9., 13.10., 11.11., 27.11., 25.12.
months <- dates_df[, paste0(var, ".month")]
dates <- dates_df[, paste0(var, ".date")]
dates_df[, paste0(var, ".hlday")] <-
ifelse( ((months == 09) & (dates == 01)) |
((months == 10) & (dates == 13)) |
((months == 11) & (dates == 27)) |
((months == 12) & (dates == 25)) ,
1, 0)
dates_df[, paste0(var, ".wkend")] <- as.numeric(
(dates_df[, paste0(var, ".wkday")] %in% c(0, 6)) |
dates_df[, paste0(var, ".hlday")] )
dates_df[, paste0(var, ".hour")] <- as.numeric(format(dates_df$.date, "%H"))
dates_df[, paste0(var, ".hour.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%H")), 3) # by work-shift
dates_df[, paste0(var, ".minute")] <- as.numeric(format(dates_df$.date, "%M"))
dates_df[, paste0(var, ".minute.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%M")), 4) # by quarter-hours
dates_df[, paste0(var, ".second")] <- as.numeric(format(dates_df$.date, "%S"))
dates_df[, paste0(var, ".second.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%S")), 4) # by quarter-hours
print(gp <- myplot_box(df=dates_df, ycol_names="PubDate.second",
xcol_name=rsp_var))
print(gp <- myplot_bar(df=dates_df, ycol_names="PubDate.second.fctr",
xcol_name=rsp_var, colorcol_name="PubDate.second.fctr"))
keep_feats <- union(keep_feats, paste(var,
c(".POSIX", ".year.fctr", ".month.fctr", ".date.fctr", ".wkday.fctr",
".wkend", ".hour.fctr", ".minute.fctr", ".second.fctr"), sep=""))
}
#myprint_df(dates_df)
return(dates_df[, keep_feats])
}
if (!is.null(glb_date_vars)) {
glb_entity_df <- cbind(glb_entity_df,
myextract_dates_df(df=glb_entity_df, vars=glb_date_vars, rsp_var=glb_rsp_var))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
paste(glb_date_vars, c("", ".POSIX"), sep=""))
}
## Warning in mean.default(X[[1L]], ...): argument is not numeric or logical:
## returning NA## Warning in mean.default(X[[2L]], ...): argument is not numeric or logical:
## returning NA## Warning in mean.default(X[[1L]], ...): argument is not numeric or logical:
## returning NA## Warning in mean.default(X[[2L]], ...): argument is not numeric or logical:
## returning NA
srt_entity_df <- orderBy(~PubDate.POSIX, glb_entity_df)
print(myplot_scatter(subset(srt_entity_df,
PubDate.POSIX < strptime("2014-09-02", "%Y-%m-%d")),
xcol_name="PubDate.POSIX", ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var
))
# Create features that measure the gap between previous timestamp in the data
require(zoo)## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericpd = as.POSIXlt(srt_entity_df$PubDate)
z = zoo(as.numeric(pd))
srt_entity_df[, "PubDate.zoo"] <- z
print(head(srt_entity_df))## NewsDesk SectionName SubsectionName
## 33 Science Health
## 32 Foreign World Asia Pacific
## 31 Multimedia
## 30 Culture Arts
## 29 Business Business Day Dealbook
## 28 Magazine Magazine
## Headline
## 33 Don't Catch What Ails Your House
## 32 Ukraine Conflict Has Been a Lift for China, Scholars Say
## 31 Revisiting Life and Death in Africa
## 30 Fabio Luisi Has a New Gig
## 29 Heineken to Sell Mexican Packaging Unit to Crown Holdings
## 28 Behind the Cover Story: Emily Bazelon on Medical Abortion Through the Mail
## Snippet
## 33 It doesnt take a flood to encourage the growth of mold in a home. A moist environment will do. A runny nose, coughing and all the rest typically follow.
## 32 As the United States and the European Union have imposed sanctions on Russia over the unrest in eastern Ukraine, China has been able to stand apart and gain concrete advantages, experts on foreign policy say.
## 31 Yunghi Kim went to Somalia 20 years ago expecting to cover a famine. She found herself instead in a war zone.
## 30 The music director of the Zurich Opera and principal conductor of the Metropolitan Opera will be named principal conductor of the Danish National Symphony Orchestra.
## 29 The deal values the container unit Empaque at about $1.2 billion and would make Crown Holdings the second-largest beverage can producer in North America.
## 28 Emily Bazelon, a contributing writer for the magazine, wrote this weeks cover story about the online distribution of medical abortions. Here she discusses reporting on a group of activists working to provide medical abortions through the mail.
## Abstract
## 33 It doesnt take a flood to encourage the growth of mold in a home. A moist environment will do. A runny nose, coughing and all the rest typically follow.
## 32 As the United States and the European Union have imposed sanctions on Russia over the unrest in eastern Ukraine, China has been able to stand apart and gain concrete advantages, experts on foreign policy say.
## 31 Yunghi Kim went to Somalia 20 years ago expecting to cover a famine. She found herself instead in a war zone.
## 30 The music director of the Zurich Opera and principal conductor of the Metropolitan Opera will be named principal conductor of the Danish National Symphony Orchestra.
## 29 The deal values the container unit Empaque at about $1.2 billion and would make Crown Holdings the second-largest beverage can producer in North America.
## 28 Emily Bazelon, a contributing writer for the magazine, wrote this weeks cover story about the online distribution of medical abortions. Here she discusses reporting on a group of activists working to provide medical abortions through the mail.
## WordCount PubDate Popular UniqueID .src Popular.fctr
## 33 962 2014-09-01 00:01:32 1 33 Train Y
## 32 529 2014-09-01 02:48:41 0 32 Train N
## 31 832 2014-09-01 03:00:15 0 31 Train N
## 30 166 2014-09-01 04:00:06 0 30 Train N
## 29 442 2014-09-01 04:11:20 0 29 Train N
## 28 1190 2014-09-01 05:00:26 0 28 Train N
## PubDate.POSIX PubDate.year.fctr PubDate.month.fctr
## 33 2014-09-01 00:01:32 2014 09
## 32 2014-09-01 02:48:41 2014 09
## 31 2014-09-01 03:00:15 2014 09
## 30 2014-09-01 04:00:06 2014 09
## 29 2014-09-01 04:11:20 2014 09
## 28 2014-09-01 05:00:26 2014 09
## PubDate.date.fctr PubDate.wkday.fctr PubDate.wkend PubDate.hour.fctr
## 33 (0.97,7] 1 1 (-0.023,7.67]
## 32 (0.97,7] 1 1 (-0.023,7.67]
## 31 (0.97,7] 1 1 (-0.023,7.67]
## 30 (0.97,7] 1 1 (-0.023,7.67]
## 29 (0.97,7] 1 1 (-0.023,7.67]
## 28 (0.97,7] 1 1 (-0.023,7.67]
## PubDate.minute.fctr PubDate.second.fctr PubDate.zoo
## 33 (-0.059,14.8] (29.5,44.2] 1409544092
## 32 (44.2,59.1] (29.5,44.2] 1409554121
## 31 (-0.059,14.8] (14.8,29.5] 1409554815
## 30 (-0.059,14.8] (-0.059,14.8] 1409558406
## 29 (-0.059,14.8] (14.8,29.5] 1409559080
## 28 (-0.059,14.8] (14.8,29.5] 1409562026print(myplot_scatter(subset(srt_entity_df,
PubDate.POSIX < strptime("2014-09-02", "%Y-%m-%d")),
xcol_name="PubDate.zoo", ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var
))## Don't know how to automatically pick scale for object of type zoo. Defaulting to continuous
n = nrow(srt_entity_df)
b = zoo(, seq(n))
last1 = as.numeric(merge(z-lag(z, -1), b, all = TRUE))
srt_entity_df[, "PubDate.last1"] <- last1
srt_entity_df[is.na(srt_entity_df$PubDate.last1), "PubDate.last1"] <- 0
srt_entity_df[, "PubDate.last1.log"] <- log(1 + srt_entity_df[, "PubDate.last1"])
print(gp <- myplot_box(df=subset(srt_entity_df, PubDate.last1.log > 0),
ycol_names="PubDate.last1.log",
xcol_name=glb_rsp_var))
last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
srt_entity_df[, "PubDate.last10"] <- last10
srt_entity_df[is.na(srt_entity_df$PubDate.last10), "PubDate.last10"] <- 0
srt_entity_df[, "PubDate.last10.log"] <- log(1 + srt_entity_df[, "PubDate.last10"])
print(gp <- myplot_box(df=subset(srt_entity_df, PubDate.last10.log > 0),
ycol_names="PubDate.last10.log",
xcol_name=glb_rsp_var))
last100 = as.numeric(merge(z-lag(z, -100), b, all = TRUE))
srt_entity_df[, "PubDate.last100"] <- last100
srt_entity_df[is.na(srt_entity_df$PubDate.last100), "PubDate.last100"] <- 0
srt_entity_df[, "PubDate.last100.log"] <- log(1 + srt_entity_df[, "PubDate.last100"])
print(gp <- myplot_box(df=subset(srt_entity_df, PubDate.last100.log > 0),
ycol_names="PubDate.last100.log",
xcol_name=glb_rsp_var))
sav_entity_df <- glb_entity_df
glb_entity_df <- srt_entity_df
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c("PubDate.zoo", "PubDate.last1", "PubDate.last10", "PubDate.last100"))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
# check distribution of all numeric data
dsp_numeric_vars_dstrb <- function(vars_lst) {
for (var in vars_lst) {
print(sprintf("var: %s", var))
gp <- myplot_box(df=glb_entity_df, ycol_names=var, xcol_name=glb_rsp_var)
if (inherits(glb_entity_df[, var], "factor"))
gp <- gp + facet_wrap(reformulate(var))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_entity_df),
# union(myfind_chr_cols_df(glb_entity_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_entity_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>.log=log(1 + <col.name>),
WordCount.log = log(1 + WordCount),
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
.rnorm=rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newent_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
# Add WordCount.log since WordCount is not distributed normally
glb_entity_df <- add_new_diag_feats(glb_entity_df)## Loading required package: plyrprint("Replacing WordCount with WordCount.log in potential feature set")## [1] "Replacing WordCount with WordCount.log in potential feature set"glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, "WordCount")
# Remove PubDate.year since all entity data is from 2014
# Remove PubDate.month.fctr since all newent data is from December
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c("PubDate.year", "PubDate.month.fctr"))
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_vars_dstrb(setdiff(names(glb_entity_df),
union(myfind_chr_cols_df(glb_entity_df),
union(glb_rsp_var_raw,
union(glb_rsp_var, glb_exclude_vars_as_features)))))## [1] "var: PubDate.year.fctr"
## [1] "var: PubDate.date.fctr"
## [1] "var: PubDate.wkday.fctr"
## [1] "var: PubDate.wkend"
## [1] "var: PubDate.hour.fctr"
## [1] "var: PubDate.minute.fctr"
## [1] "var: PubDate.second.fctr"
## [1] "var: PubDate.last1.log"
## [1] "var: PubDate.last10.log"
## [1] "var: PubDate.last100.log"
## [1] "var: WordCount.log"
## [1] "var: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnent_df, select=-c(col_symbol)))
# Check for glb_newent_df & glb_trnent_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnent_df, <col1_name> == max(glb_trnent_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnent_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnent_df[which.max(glb_trnent_df$<col_name>),])
# print(<col_name>_freq_glb_trnent_df <- mycreate_tbl_df(glb_trnent_df, "<col_name>"))
# print(which.min(table(glb_trnent_df$<col_name>)))
# print(which.max(table(glb_trnent_df$<col_name>)))
# print(which.max(table(glb_trnent_df$<col1_name>, glb_trnent_df$<col2_name>)[, 2]))
# print(table(glb_trnent_df$<col1_name>, glb_trnent_df$<col2_name>))
# print(table(is.na(glb_trnent_df$<col1_name>), glb_trnent_df$<col2_name>))
# print(table(sign(glb_trnent_df$<col1_name>), glb_trnent_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnent_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnent_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnent_df <-
# mycreate_xtab_df(glb_trnent_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnent_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnent_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnent_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnent_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnent_df$<col1_name>, glb_trnent_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnent_df$<col1_name>.NA, glb_trnent_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnent_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnent_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnent_df, Symbol %in% c("KO", "PG")),
# "Date.my", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.Date("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.Date("1983-01-01")))
# )
# print(myplot_scatter(glb_entity_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_entity_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_entity_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cleanse.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 12.185 30.473 18.288
## 3 cleanse.data 2 1 30.474 NA NAStep 2.1: cleanse data
# Options:
# 1. Not fill missing vars
# 2. Fill missing numerics with a different algorithm
# 3. Fill missing chars with data based on clusters
dsp_problem_data(glb_entity_df)## [1] "numeric data missing in : "
## WordCount Popular UniqueID
## 0 1870 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 1870 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 0
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 0 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 0
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 0 0 0
## PubDate.last100 PubDate.last100.log WordCount.log
## 0 0 0
## .rnorm
## 0
## [1] "numeric data w/ 0s in : "
## WordCount Popular UniqueID
## 109 5439 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 0 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 378
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 7624 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 11
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 11 10 10
## PubDate.last100 PubDate.last100.log WordCount.log
## 100 100 109
## .rnorm
## 0
## [1] "numeric data w/ Infs in : "
## WordCount Popular UniqueID
## 0 0 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 0 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 0
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 0 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 0
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 0 0 0
## PubDate.last100 PubDate.last100.log WordCount.log
## 0 0 0
## .rnorm
## 0
## [1] "numeric data w/ NaNs in : "
## WordCount Popular UniqueID
## 0 0 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 0 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 0
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 0 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 0
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 0 0 0
## PubDate.last100 PubDate.last100.log WordCount.log
## 0 0 0
## .rnorm
## 0
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate
## 17 0warning("Forcing ", nrow(subset(glb_entity_df, WordCount.log == 0)),
" obs with WordCount.log 0s to NA")## Warning: Forcing 109 obs with WordCount.log 0s to NAglb_entity_df[glb_entity_df$WordCount.log == 0, "WordCount.log"] <- NA
dsp_problem_data(glb_entity_df)## [1] "numeric data missing in : "
## WordCount Popular UniqueID
## 0 1870 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 1870 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 0
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 0 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 0
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 0 0 0
## PubDate.last100 PubDate.last100.log WordCount.log
## 0 0 109
## .rnorm
## 0
## [1] "numeric data w/ 0s in : "
## WordCount Popular UniqueID
## 109 5439 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 0 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 378
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 7624 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 11
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 11 10 10
## PubDate.last100 PubDate.last100.log WordCount.log
## 100 100 0
## .rnorm
## 0
## [1] "numeric data w/ Infs in : "
## WordCount Popular UniqueID
## 0 0 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 0 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 0
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 0 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 0
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 0 0 0
## PubDate.last100 PubDate.last100.log WordCount.log
## 0 0 0
## .rnorm
## 0
## [1] "numeric data w/ NaNs in : "
## WordCount Popular UniqueID
## 0 0 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 0 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 0
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 0 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 0
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 0 0 0
## PubDate.last100 PubDate.last100.log WordCount.log
## 0 0 0
## .rnorm
## 0
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate
## 17 0dsp_catgs <- function() {
print("NewsDesk:")
print(table(glb_entity_df$NewsDesk))
print("SectionName:")
print(table(glb_entity_df$SectionName))
print("SubsectionName:")
print(table(glb_entity_df$SubsectionName))
}
sel_obs <- function(Popular=NULL,
NewsDesk=NULL, SectionName=NULL, SubsectionName=NULL,
Headline.contains=NULL, Snippet.contains=NULL, Abstract.contains=NULL,
Headline.pfx=NULL, NewsDesk.nb=NULL) {
tmp_entity_df <- glb_entity_df
# Does not work for Popular == NAs ???
if (!is.null(Popular)) {
if (is.na(Popular))
tmp_entity_df <- tmp_entity_df[is.na(tmp_entity_df$Popular), ] else
tmp_entity_df <- tmp_entity_df[tmp_entity_df$Popular == Popular, ]
}
if (!is.null(NewsDesk))
tmp_entity_df <- tmp_entity_df[tmp_entity_df$NewsDesk == NewsDesk, ]
if (!is.null(SectionName))
tmp_entity_df <- tmp_entity_df[tmp_entity_df$SectionName == SectionName, ]
if (!is.null(SubsectionName))
tmp_entity_df <- tmp_entity_df[tmp_entity_df$SubsectionName == SubsectionName, ]
if (!is.null(Headline.contains))
tmp_entity_df <-
tmp_entity_df[grep(Headline.contains, tmp_entity_df$Headline), ]
if (!is.null(Snippet.contains))
tmp_entity_df <-
tmp_entity_df[grep(Snippet.contains, tmp_entity_df$Snippet), ]
if (!is.null(Abstract.contains))
tmp_entity_df <-
tmp_entity_df[grep(Abstract.contains, tmp_entity_df$Abstract), ]
if (!is.null(Headline.pfx)) {
if (length(grep("Headline.pfx", names(tmp_entity_df), fixed=TRUE, value=TRUE))
> 0) tmp_entity_df <-
tmp_entity_df[tmp_entity_df$Headline.pfx == Headline.pfx, ] else
warning("glb_entity_df does not contain Headline.pfx; ignoring that filter")
}
if (!is.null(NewsDesk.nb)) {
if (any(grepl("NewsDesk.nb", names(tmp_entity_df), fixed=TRUE)) > 0)
tmp_entity_df <-
tmp_entity_df[tmp_entity_df$NewsDesk.nb == NewsDesk.nb, ] else
warning("glb_entity_df does not contain NewsDesk.nb; ignoring that filter")
}
return(glb_entity_df$UniqueID %in% tmp_entity_df$UniqueID)
}
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_entity_df[sel_obs(...),
union(c("UniqueID", "Popular", "Headline"), cols), FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_tbl <- function(...) {
tmp_entity_df <- glb_entity_df[sel_obs(...), ]
tmp_tbl <- table(tmp_entity_df$NewsDesk,
tmp_entity_df$SectionName,
tmp_entity_df$SubsectionName,
tmp_entity_df$Popular, useNA="ifany")
#print(names(tmp_tbl))
#print(dimnames(tmp_tbl))
print(tmp_tbl)
}
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_entity_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_entity_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Create myCategory <- NewsDesk#SectionName#SubsectionName
# Fix some data before merging categories
glb_entity_df[sel_obs(Headline.contains="Your Turn:", NewsDesk=""),
"NewsDesk"] <- "Styles"
glb_entity_df[sel_obs(Headline.contains="School", NewsDesk="", SectionName="U.S.",
SubsectionName=""),
"SubsectionName"] <- "Education"
glb_entity_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
"SectionName"] <- "Business Day"
glb_entity_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
"SubsectionName"] <- "Small Business"
glb_entity_df[sel_obs(Headline.contains="Readers Respond:"),
"SectionName"] <- "Opinion"
glb_entity_df[sel_obs(Headline.contains="Readers Respond:"),
"SubsectionName"] <- "Room For Debate"
# glb_entity_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName="", Popular=NA),
# "SubsectionName"] <- "Small Business"
# print(glb_entity_df[glb_entity_df$UniqueID %in% c(7973),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_entity_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName=""),
# "SectionName"] <- "Technology"
# print(glb_entity_df[glb_entity_df$UniqueID %in% c(5076, 5736, 5924, 5911, 6532),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_entity_df[sel_obs(SectionName="Health"),
# "NewsDesk"] <- "Science"
# glb_entity_df[sel_obs(SectionName="Travel"),
# "NewsDesk"] <- "Travel"
#
# glb_entity_df[sel_obs(SubsectionName="Fashion & Style"),
# "SectionName"] <- ""
# glb_entity_df[sel_obs(SubsectionName="Fashion & Style"),
# "SubsectionName"] <- ""
# glb_entity_df[sel_obs(NewsDesk="Styles", SectionName="", SubsectionName="", Popular=1),
# "SectionName"] <- "U.S."
# print(glb_entity_df[glb_entity_df$UniqueID %in% c(5486),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
glb_entity_df$myCategory <- paste(glb_entity_df$NewsDesk,
glb_entity_df$SectionName,
glb_entity_df$SubsectionName,
sep="#")
dsp_obs( Headline.contains="Music:"
#,NewsDesk=""
#,SectionName=""
#,SubsectionName="Fashion & Style"
#,Popular=1 #NA
,cols= c("UniqueID", "Headline", "Popular", "myCategory",
"NewsDesk", "SectionName", "SubsectionName"),
all=TRUE)## UniqueID Popular
## 305 305 0
## 844 844 1
## 1331 1331 0
## 1974 1974 0
## 2563 2563 0
## 3091 3091 0
## 3589 3589 0
## 4631 4631 0
## 5125 5125 0
## 5630 5630 0
## 6095 6095 0
## 6513 6513 1
## 6927 6927 NA
## 7473 7473 NA
## 7931 7931 NA
## 8217 8217 NA
## Headline
## 305 Friday Night Music: Lucius Covers John Lennon
## 844 Friday Night Music: Cheryl Wheeler
## 1331 Friday Night Music: Cheryl Wheeler, Summer Fly
## 1974 Friday Night Music: Quilt
## 2563 Friday Night Music: Lucius in Asheville
## 3091 Friday Night Music: Sarah Jarosz and the Milk Carton Kids
## 3589 Friday Night Music: Lucius Covers the Kinks
## 4631 Friday Night Music: Amason
## 5125 Friday Night Music: Suzanne Vega, Jacob and the Angel
## 5630 Friday Night Music: Suzanne Vega, I Never Wear White
## 6095 Friday Night Music: Jessica Hernandez and the Deltas
## 6513 Saturday Morning Music: Stay Gold
## 6927 Friday Night Music: Lucius, Monsters
## 7473 Friday Night Music: Peter Gabriel, 1993
## 7931 Friday Night Music: The Roches, Winter Wonderland
## 8217 Friday Night Music: Sarah Jarosz and Aoife O'Donovan
## myCategory NewsDesk SectionName SubsectionName
## 305 OpEd#Opinion# OpEd Opinion
## 844 OpEd#Opinion# OpEd Opinion
## 1331 OpEd#Opinion# OpEd Opinion
## 1974 OpEd#Opinion# OpEd Opinion
## 2563 OpEd#Opinion# OpEd Opinion
## 3091 OpEd#Opinion# OpEd Opinion
## 3589 OpEd#Opinion# OpEd Opinion
## 4631 OpEd#Opinion# OpEd Opinion
## 5125 OpEd#Opinion# OpEd Opinion
## 5630 OpEd#Opinion# OpEd Opinion
## 6095 OpEd#Opinion# OpEd Opinion
## 6513 OpEd#Opinion# OpEd Opinion
## 6927 OpEd#Opinion# OpEd Opinion
## 7473 #Opinion# Opinion
## 7931 OpEd#Opinion# OpEd Opinion
## 8217 OpEd#Opinion# OpEd Opiniondsp_obs( Headline.contains="."
,NewsDesk=""
,SectionName="Opinion"
,SubsectionName=""
#,Popular=1 #NA
,cols= c("UniqueID", "Headline", "Popular", "myCategory",
"NewsDesk", "SectionName", "SubsectionName"),
all=TRUE)## UniqueID Popular
## 516 516 0
## 918 918 0
## 7473 7473 NA
## 7445 7445 NA
## 7419 7419 NA
## 7505 7505 NA
## 7509 7509 NA
## Headline
## 516 This Is Life Among the Roma, Europes Forgotten People
## 918 What Might Happen If Iran Becomes America's Covert Ally?
## 7473 Friday Night Music: Peter Gabriel, 1993
## 7445 Senate Committee Bothered to Authorize War Against Islamic State
## 7419 Joe on WNYCs Money Talking
## 7505 Rev. Dr. William Barber II on Todays Protest Movements
## 7509 Did Salaita Cross the Line of Civility?
## myCategory NewsDesk SectionName SubsectionName
## 516 #Opinion# Opinion
## 918 #Opinion# Opinion
## 7473 #Opinion# Opinion
## 7445 #Opinion# Opinion
## 7419 #Opinion# Opinion
## 7505 #Opinion# Opinion
## 7509 #Opinion# Opinion# Merge some categories
glb_entity_df$myCategory <-
plyr::revalue(glb_entity_df$myCategory, c(
"#Business Day#Dealbook" = "Business#Business Day#Dealbook",
"#Business Day#Small Business" = "Business#Business Day#Small Business",
"#Crosswords/Games#" = "Business#Crosswords/Games#",
"Business##" = "Business#Technology#",
"#Open#" = "Business#Technology#",
"#Technology#" = "Business#Technology#",
"#Arts#" = "Culture#Arts#",
"Culture##" = "Culture#Arts#",
"#World#Asia Pacific" = "Foreign#World#Asia Pacific",
"Foreign##" = "Foreign#World#",
"#N.Y. / Region#" = "Metro#N.Y. / Region#",
"#Opinion#" = "OpEd#Opinion#",
"OpEd##" = "OpEd#Opinion#",
"#Health#" = "Science#Health#",
"Science##" = "Science#Health#",
"Styles##" = "Styles##Fashion",
"Styles#Health#" = "Science#Health#",
"Styles#Style#Fashion & Style" = "Styles##Fashion",
"#Travel#" = "Travel#Travel#",
"Magazine#Magazine#" = "myOther",
"National##" = "myOther",
"National#U.S.#Politics" = "myOther",
"Sports##" = "myOther",
"Sports#Sports#" = "myOther",
"#U.S.#" = "myOther",
# "Business##Small Business" = "Business#Business Day#Small Business",
#
# "#Opinion#" = "#Opinion#Room For Debate",
"##" = "##"
# "Business##" = "Business#Business Day#Dealbook",
# "Foreign#World#" = "Foreign##",
# "#Open#" = "Other",
# "#Opinion#The Public Editor" = "OpEd#Opinion#",
# "Styles#Health#" = "Styles##",
# "Styles#Style#Fashion & Style" = "Styles##",
# "#U.S.#" = "#U.S.#Education",
))
ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
mycreate_sqlxtab_df(glb_entity_df,
c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
myCategory + NewsDesk + SectionName + SubsectionName ~
Popular.fctr, sum, value.var=".n"))
myprint_df(ctgry_cast_df)## myCategory NewsDesk SectionName SubsectionName
## 33 OpEd#Opinion# OpEd Opinion
## 36 Science#Health# Science Health
## 1 ##
## 11 Business#Crosswords/Games# Business Crosswords/Games
## 40 Styles#U.S.# Styles U.S.
## 7 Business#Business Day#Dealbook Business Business Day Dealbook
## N Y NA
## 33 113 407 141
## 36 73 119 55
## 1 1163 110 338
## 11 19 103 38
## 40 77 100 62
## 7 864 88 291
## myCategory NewsDesk SectionName SubsectionName N Y NA
## 35 Science#Health# Science 0 2 2
## 5 #U.S.#Education U.S. Education 325 0 90
## 16 Culture#Arts# Arts 0 0 11
## 13 Business#Technology# Technology 0 0 1
## 27 myOther National 2 0 0
## 39 Styles##Fashion Styles Style Fashion & Style 2 0 0
## myCategory NewsDesk SectionName SubsectionName N Y NA
## 27 myOther National 2 0 0
## 28 myOther National U.S. Politics 2 0 0
## 29 myOther Sports 1 0 0
## 30 myOther Sports Sports 1 0 0
## 37 Science#Health# Styles Health 1 0 0
## 39 Styles##Fashion Styles Style Fashion & Style 2 0 0write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
row.names=FALSE)
print(ctgry_sum_tbl <- table(glb_entity_df$myCategory, glb_entity_df[, glb_rsp_var],
useNA="ifany"))##
## N Y <NA>
## ## 1163 110 338
## #Multimedia# 139 2 52
## #Opinion#Room For Debate 69 7 24
## #Opinion#The Public Editor 4 16 10
## #U.S.#Education 325 0 90
## Business#Business Day#Dealbook 864 88 304
## Business#Business Day#Small Business 135 5 42
## Business#Crosswords/Games# 20 103 42
## Business#Technology# 288 51 113
## Culture#Arts# 626 50 244
## Foreign#World# 172 0 47
## Foreign#World#Asia Pacific 200 3 56
## Metro#N.Y. / Region# 181 17 67
## myOther 38 0 3
## OpEd#Opinion# 115 408 164
## Science#Health# 74 122 57
## Styles##Fashion 118 1 15
## Styles#U.S.# 77 100 62
## Travel#Travel# 116 1 35
## TStyle## 715 9 105dsp_chisq.test <- function(...) {
sel_df <- glb_entity_df[sel_obs(...) &
!is.na(glb_entity_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_entity_df[!is.na(glb_entity_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_vars, "Popular")],
sel_df[, c(glb_id_vars, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_entity_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_entity_df$NewsDesk, glb_entity_df$SectionName))
# print(table(glb_entity_df$SectionName, glb_entity_df$SubsectionName))
# print(table(glb_entity_df$NewsDesk, glb_entity_df$SectionName, glb_entity_df$SubsectionName))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c("NewsDesk", "SectionName", "SubsectionName"))
# Copy Headline into Snipper & Abstract if they are empty
print(glb_entity_df[nchar(glb_entity_df[, "Snippet"]) == 0, c("Headline", "Snippet")])## Headline
## 2838 First Draft Focus: Off to Raise Money for Democrats
## 3728 Verbatim: Obama as Supreme Court Justice?
## 4904 Election 2014: Live Coverage
## 4994 Election 2014: Live Coverage
## 5065 First Draft Focus: Honoring a Civil War Hero
## 5029 First Draft Focus: Perry's Day in Court
## 5160 Supreme Court to Hear New Health Law Challenge
## 5254 Verbatim: Will Rick Perry Run?
## 5472 First Draft Focus: A Red Carpet Welcome
## 7164 Does Torture Work? C.I.A.'s Claims vs. Senate Panel's Findings
## 7129 First Draft Focus: Pass a Bill
## 7368 Verbatim: The People's Priorities
## 7364 First Draft Focus: Three Wise Men
## Snippet
## 2838
## 3728
## 4904
## 4994
## 5065
## 5029
## 5160
## 5254
## 5472
## 7164
## 7129
## 7368
## 7364print(glb_entity_df[glb_entity_df$Headline == glb_entity_df$Snippet,
c("UniqueID", "Headline", "Snippet")])## [1] UniqueID Headline Snippet
## <0 rows> (or 0-length row.names)glb_entity_df[nchar(glb_entity_df[, "Snippet"]) == 0, "Snippet"] <-
glb_entity_df[nchar(glb_entity_df[, "Snippet"]) == 0, "Headline"]
print(glb_entity_df[nchar(glb_entity_df[, "Abstract"]) == 0, c("Headline", "Abstract")])## Headline
## 2838 First Draft Focus: Off to Raise Money for Democrats
## 3728 Verbatim: Obama as Supreme Court Justice?
## 4904 Election 2014: Live Coverage
## 4994 Election 2014: Live Coverage
## 5065 First Draft Focus: Honoring a Civil War Hero
## 5029 First Draft Focus: Perry's Day in Court
## 5160 Supreme Court to Hear New Health Law Challenge
## 5254 Verbatim: Will Rick Perry Run?
## 5472 First Draft Focus: A Red Carpet Welcome
## 7164 Does Torture Work? C.I.A.'s Claims vs. Senate Panel's Findings
## 7129 First Draft Focus: Pass a Bill
## 7368 Verbatim: The People's Priorities
## 7364 First Draft Focus: Three Wise Men
## 7329 Obama Works the Phones to Get Funding Deal Done
## 7315 House Democrats Vent Frustration With White House
## 7310 Funding Bill Hangs in Balance as House Votes
## 7309 Spending Bill Passes House With Democratic Support
## Abstract
## 2838
## 3728
## 4904
## 4994
## 5065
## 5029
## 5160
## 5254
## 5472
## 7164
## 7129
## 7368
## 7364
## 7329
## 7315
## 7310
## 7309print(glb_entity_df[glb_entity_df$Headline == glb_entity_df$Abstract,
c("UniqueID", "Headline", "Abstract")])## [1] UniqueID Headline Abstract
## <0 rows> (or 0-length row.names)glb_entity_df[nchar(glb_entity_df[, "Abstract"]) == 0, "Abstract"] <-
glb_entity_df[nchar(glb_entity_df[, "Abstract"]) == 0, "Headline"]
# WordCount_0_df <- subset(glb_entity_df, WordCount == 0)
# table(WordCount_0_df$Popular, WordCount_0_df$WordCount, useNA="ifany")
# myprint_df(WordCount_0_df[,
# c("UniqueID", "Popular", "WordCount", "Headline")])
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 cleanse.data 2 1 30.474 34.314 3.84
## 4 manage.missing.data 2 2 34.314 NA NAStep 2.2: manage missing data
# print(sapply(names(glb_trnent_df), function(col) sum(is.na(glb_trnent_df[, col]))))
# print(sapply(names(glb_newent_df), function(col) sum(is.na(glb_newent_df[, col]))))
# glb_trnent_df <- na.omit(glb_trnent_df)
# glb_newent_df <- na.omit(glb_newent_df)
# df[is.na(df)] <- 0
dsp_problem_data(glb_entity_df)## [1] "numeric data missing in : "
## WordCount Popular UniqueID
## 0 1870 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 1870 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 0
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 0 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 0
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 0 0 0
## PubDate.last100 PubDate.last100.log WordCount.log
## 0 0 109
## .rnorm
## 0
## [1] "numeric data w/ 0s in : "
## WordCount Popular UniqueID
## 109 5439 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 0 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 378
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 7624 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 11
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 11 10 10
## PubDate.last100 PubDate.last100.log WordCount.log
## 100 100 0
## .rnorm
## 0
## [1] "numeric data w/ Infs in : "
## WordCount Popular UniqueID
## 0 0 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 0 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 0
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 0 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 0
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 0 0 0
## PubDate.last100 PubDate.last100.log WordCount.log
## 0 0 0
## .rnorm
## 0
## [1] "numeric data w/ NaNs in : "
## WordCount Popular UniqueID
## 0 0 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 0 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 0
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 0 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 0
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 0 0 0
## PubDate.last100 PubDate.last100.log WordCount.log
## 0 0 0
## .rnorm
## 0
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2407 2883 6156 0 0
## Abstract PubDate myCategory
## 0 0 0# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_entity_df[, setdiff(names(glb_entity_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
return(out_impent_df[, "WordCount.log"])
}
if (glb_impute_na_data)
glb_entity_df[, "WordCount.log"] <- glb_impute_missing_data()## Loading required package: mice
## Loading required package: Rcpp
## Loading required package: lattice
## mice 2.22 2014-06-10## [1] "Summary before imputation: "
## PubDate.year.fctr PubDate.date.fctr PubDate.wkday.fctr PubDate.wkend
## 2014:8402 (0.97,7]:1981 0: 378 Min. :0.0000
## (7,13] :1757 1:1605 1st Qu.:0.0000
## (13,19] :1808 2:1559 Median :0.0000
## (19,25] :1650 3:1614 Mean :0.0926
## (25,31] :1206 4:1539 3rd Qu.:0.0000
## 5:1470 Max. :1.0000
## 6: 237
## PubDate.hour.fctr PubDate.minute.fctr PubDate.second.fctr
## (-0.023,7.67]:1610 (-0.059,14.8]:3119 (-0.059,14.8]:2134
## (7.67,15.3] :4484 (14.8,29.5] :1671 (14.8,29.5] :2063
## (15.3,23] :2308 (29.5,44.2] :1995 (29.5,44.2] :2112
## (44.2,59.1] :1617 (44.2,59.1] :2093
##
##
##
## PubDate.last1.log PubDate.last10.log PubDate.last100.log WordCount.log
## Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. :0.6932
## 1st Qu.: 5.263 1st Qu.: 8.516 1st Qu.:11.37 1st Qu.:5.2679
## Median : 6.292 Median : 8.868 Median :11.43 Median :5.9480
## Mean : 6.094 Mean : 9.048 Mean :11.49 Mean :5.8263
## 3rd Qu.: 7.126 3rd Qu.: 9.424 3rd Qu.:11.78 3rd Qu.:6.6067
## Max. :10.875 Max. :11.744 Max. :12.95 Max. :9.2977
## NA's :109
## .rnorm myCategory
## Min. :-3.881663 Length:8402
## 1st Qu.:-0.665043 Class :character
## Median :-0.004510 Mode :character
## Mean :-0.006807
## 3rd Qu.: 0.664125
## Max. : 3.356092
##
##
## iter imp variable
## 1 1 WordCount.log
## 1 2 WordCount.log
## 1 3 WordCount.log
## 1 4 WordCount.log
## 1 5 WordCount.log
## 2 1 WordCount.log
## 2 2 WordCount.log
## 2 3 WordCount.log
## 2 4 WordCount.log
## 2 5 WordCount.log
## 3 1 WordCount.log
## 3 2 WordCount.log
## 3 3 WordCount.log
## 3 4 WordCount.log
## 3 5 WordCount.log
## 4 1 WordCount.log
## 4 2 WordCount.log
## 4 3 WordCount.log
## 4 4 WordCount.log
## 4 5 WordCount.log
## 5 1 WordCount.log
## 5 2 WordCount.log
## 5 3 WordCount.log
## 5 4 WordCount.log
## 5 5 WordCount.log
## PubDate.year.fctr PubDate.date.fctr PubDate.wkday.fctr PubDate.wkend
## 2014:8402 (0.97,7]:1981 0: 378 Min. :0.0000
## (7,13] :1757 1:1605 1st Qu.:0.0000
## (13,19] :1808 2:1559 Median :0.0000
## (19,25] :1650 3:1614 Mean :0.0926
## (25,31] :1206 4:1539 3rd Qu.:0.0000
## 5:1470 Max. :1.0000
## 6: 237
## PubDate.hour.fctr PubDate.minute.fctr PubDate.second.fctr
## (-0.023,7.67]:1610 (-0.059,14.8]:3119 (-0.059,14.8]:2134
## (7.67,15.3] :4484 (14.8,29.5] :1671 (14.8,29.5] :2063
## (15.3,23] :2308 (29.5,44.2] :1995 (29.5,44.2] :2112
## (44.2,59.1] :1617 (44.2,59.1] :2093
##
##
##
## PubDate.last1.log PubDate.last10.log PubDate.last100.log WordCount.log
## Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. :0.6931
## 1st Qu.: 5.263 1st Qu.: 8.516 1st Qu.:11.37 1st Qu.:5.2679
## Median : 6.292 Median : 8.868 Median :11.43 Median :5.9506
## Mean : 6.094 Mean : 9.048 Mean :11.49 Mean :5.8273
## 3rd Qu.: 7.126 3rd Qu.: 9.424 3rd Qu.:11.78 3rd Qu.:6.6067
## Max. :10.875 Max. :11.744 Max. :12.95 Max. :9.2977
##
## .rnorm myCategory
## Min. :-3.881663 Length:8402
## 1st Qu.:-0.665043 Class :character
## Median :-0.004510 Mode :character
## Mean :-0.006807
## 3rd Qu.: 0.664125
## Max. : 3.356092
## dsp_problem_data(glb_entity_df)## [1] "numeric data missing in : "
## WordCount Popular UniqueID
## 0 1870 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 1870 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 0
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 0 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 0
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 0 0 0
## PubDate.last100 PubDate.last100.log WordCount.log
## 0 0 0
## .rnorm
## 0
## [1] "numeric data w/ 0s in : "
## WordCount Popular UniqueID
## 109 5439 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 0 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 378
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 7624 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 11
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 11 10 10
## PubDate.last100 PubDate.last100.log WordCount.log
## 100 100 0
## .rnorm
## 0
## [1] "numeric data w/ Infs in : "
## WordCount Popular UniqueID
## 0 0 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 0 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 0
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 0 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 0
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 0 0 0
## PubDate.last100 PubDate.last100.log WordCount.log
## 0 0 0
## .rnorm
## 0
## [1] "numeric data w/ NaNs in : "
## WordCount Popular UniqueID
## 0 0 0
## Popular.fctr PubDate.POSIX PubDate.year.fctr
## 0 0 0
## PubDate.month.fctr PubDate.date.fctr PubDate.wkday.fctr
## 0 0 0
## PubDate.wkend PubDate.hour.fctr PubDate.minute.fctr
## 0 0 0
## PubDate.second.fctr PubDate.zoo PubDate.last1
## 0 0 0
## PubDate.last1.log PubDate.last10 PubDate.last10.log
## 0 0 0
## PubDate.last100 PubDate.last100.log WordCount.log
## 0 0 0
## .rnorm
## 0
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2407 2883 6156 0 0
## Abstract PubDate myCategory
## 0 0 0glb_chunks_df <- myadd_chunk(glb_chunks_df, "encode.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 4 manage.missing.data 2 2 34.314 38.998 4.684
## 5 encode.data 2 3 38.998 NA NAStep 2.3: encode data
# map_<col_name>_df <- myimport_data(
# url="<map_url>",
# comment="map_<col_name>_df", print_diagn=TRUE)
# map_<col_name>_df <- read.csv(paste0(getwd(), "/data/<file_name>.csv"), strip.white=TRUE)
# glb_trnent_df <- mymap_codes(glb_trnent_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_newent_df <- mymap_codes(glb_newent_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_trnent_df$<col_name>.fctr <- factor(glb_trnent_df$<col_name>,
# as.factor(union(glb_trnent_df$<col_name>, glb_newent_df$<col_name>)))
# glb_newent_df$<col_name>.fctr <- factor(glb_newent_df$<col_name>,
# as.factor(union(glb_trnent_df$<col_name>, glb_newent_df$<col_name>)))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 encode.data 2 3 38.998 39.054 0.056
## 6 extract.features 3 0 39.055 NA NAStep 3.0: extract features
#```{r extract_features, cache=FALSE, eval=glb_is_textual}
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnent_df$<col_name>), -2, na.pad=TRUE)
# glb_trnent_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newent_df$<col_name>), -2, na.pad=TRUE)
# glb_newent_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newent_df[1, "<col_name>.lag.2"] <- glb_trnent_df[nrow(glb_trnent_df) - 1,
# "<col_name>"]
# glb_newent_df[2, "<col_name>.lag.2"] <- glb_trnent_df[nrow(glb_trnent_df),
# "<col_name>"]
# glb_entity_df <- mutate(glb_entity_df,
# A.has.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnent_df <- mutate(glb_trnent_df,
# )
#
# glb_newent_df <- mutate(glb_newent_df,
# )
# Create factors of string variables
print(str_vars <- myfind_chr_cols_df(glb_entity_df))## NewsDesk SectionName SubsectionName Headline
## "NewsDesk" "SectionName" "SubsectionName" "Headline"
## Snippet Abstract PubDate .src
## "Snippet" "Abstract" "PubDate" ".src"
## myCategory
## "myCategory"if (length(str_vars <- setdiff(str_vars,
glb_exclude_vars_as_features)) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_entity_df[, var])))
glb_entity_df[, paste0(var, ".fctr")] <- factor(glb_entity_df[, var],
as.factor(unique(glb_entity_df[, var])))
# glb_trnent_df[, paste0(var, ".fctr")] <- factor(glb_trnent_df[, var],
# as.factor(unique(glb_entity_df[, var])))
# glb_newent_df[, paste0(var, ".fctr")] <- factor(glb_newent_df[, var],
# as.factor(unique(glb_entity_df[, var])))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: myCategory: # of unique
## values: 20if (glb_is_textual) {
require(tm)
glb_corpus_lst <- list(); glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Building corpus for %s...", txt_var))
# Combine "new york" to "newyork"
# shd be created as a tm_map::content_transformer
txt_df <- glb_entity_df[, txt_var]
txt_df <- gsub("[Nn]ew [Dd]elhi", "newdelhi", txt_df)
txt_df <- gsub("[Nn]ew [Gg]uinea", "newguinea", txt_df)
txt_df <- gsub("[Nn]ew [Jj]ersey", "newjersey", txt_df)
txt_df <- gsub("[Nn]ew [Oo]rleans", "neworleans", txt_df)
txt_df <- gsub("[Nn]ew [Yy]ear", "newyear", txt_df)
txt_df <- gsub("[Nn]ew [Yy]ork", "newyork", txt_df)
txt_df <- gsub("[Nn]ew [Zz]ealand", "newzealand", txt_df)
if (txt_var == "Headline") {
# dsp_chisq.test(Headline.contains="[Nn]ew ")
# print(head(txt_df[grep("[Nn]ew ", txt_df)]))
# print(tail(txt_df[grep("[Nn]ew ", txt_df)]))
# print(sample(txt_df[grep("[Nn]ew ", txt_df)], 5))
# print(length(txt_df[grep("[Nn]ew ", txt_df)]))
# print(txt_df[grep("[Nn]ew ", txt_df)][01:20])
# print(txt_df[grep("[Nn]ew ", txt_df)][21:40])
# print(txt_df[grep("[Nn]ew ", txt_df)][41:60])
# print(txt_df[grep("[Nn]ew ", txt_df)][61:80])
# print(txt_df[grep("[Nn]ew ", txt_df)][81:100])
# #print(length(txt_df[grep("[Nn]ew [Zz]ealand", txt_df)]))
# dsp_chisq.test(Headline.contains="[Nn]ew [Yy]ork")
# dsp_chisq.test(Headline.contains="[Re]eport")
# dsp_chisq.test(Snippet.contains="[Re]eport")
#
# dsp_chisq.test(Headline.contains="[Ww]eek")
# dsp_chisq.test(Headline.contains="[Dd]ay")
# dsp_chisq.test(Headline.contains="[Ff]ashion")
# dsp_chisq.test(Headline.contains="[Tt]oday")
# dsp_chisq.test(Headline.contains="[Dd]ail")
# dsp_chisq.test(Headline.contains="2014")
# dsp_chisq.test(Headline.contains="2015")
glb_append_stop_words[["Headline"]] <- c(NULL)
}
if (txt_var == "Snippet") {
# dsp_chisq.test(Snippet.contains="[Nn]ew ")
# print(head(txt_df[grep("[Nn]ew ", txt_df)]))
# print(tail(txt_df[grep("[Nn]ew ", txt_df)]))
# print(sample(txt_df[grep("[Nn]ew ", txt_df)], 5))
# print(length(txt_df[grep("[Nn]ew ", txt_df)]))
# print(txt_df[grep("[Nn]ew ", txt_df)][11:20])
# print(txt_df[grep("[Nn]ew ", txt_df)][21:30])
# print(txt_df[grep("[Nn]ew ", txt_df)][31:40])
# print(txt_df[grep("[Nn]ew ", txt_df)][41:50])
# print(txt_df[grep("[Nn]ew ", txt_df)][51:60])
# #print(length(txt_df[grep("[Nn]ew [Zz]ealand", txt_df)]))
# dsp_chisq.test(Snippet.contains="[Ww]ill")
# dsp_chisq.test(Snippet.contains="[Tt]ime")
# dsp_chisq.test(Snippet.contains="[Ww]eek")
# dsp_chisq.test(Snippet.contains="[Yy]ear")
# dsp_chisq.test(Snippet.contains="[Ne]w [Yy]ork")
# dsp_chisq.test(Snippet.contains="[Cc]ompan")
# dsp_chisq.test(Snippet.contains="[Oo]ne")
# dsp_chisq.test(Snippet.contains="[Rr]eport")
# dsp_chisq.test(Snippet.contains="[Pp]resid")
# dsp_chisq.test(Snippet.contains="[Ss]aid")
# dsp_chisq.test(Snippet.contains="[Cc]an")
# dsp_chisq.test(Snippet.contains="[Dd]ay")
glb_append_stop_words[["Snippet"]] <- c(NULL)
#c("can")
}
if (txt_var == "Abstract") {
# dsp_chisq.test(Abstract.contains="[Nn]ew ")
# print(head(txt_df[grep("[Nn]ew ", txt_df)]))
# print(tail(txt_df[grep("[Nn]ew ", txt_df)]))
# print(sample(txt_df[grep("[Nn]ew ", txt_df)], 5))
# print(length(txt_df[grep("[Nn]ew ", txt_df)]))
# print(txt_df[grep("[Nn]ew ", txt_df)][11:20])
# print(txt_df[grep("[Nn]ew ", txt_df)][21:30])
# print(txt_df[grep("[Nn]ew ", txt_df)][31:40])
# print(txt_df[grep("[Nn]ew ", txt_df)][41:50])
# print(txt_df[grep("[Nn]ew ", txt_df)][51:60])
# #print(length(txt_df[grep("[Nn]ew [Zz]ealand", txt_df)]))
#
# dsp_chisq.test(Abstract.contains="[Ww]ill")
# dsp_chisq.test(Abstract.contains="[Tt]ime")
# dsp_chisq.test(Abstract.contains="[Ww]eek")
# dsp_chisq.test(Abstract.contains="[Yy]ear")
# dsp_chisq.test(Abstract.contains="[Ne]w [Yy]ork")
# dsp_chisq.test(Abstract.contains="[Cc]ompan")
# dsp_chisq.test(Abstract.contains="[Oo]ne")
# dsp_chisq.test(Abstract.contains="[Rr]eport")
# dsp_chisq.test(Abstract.contains="[Pp]resid")
#
# dsp_chisq.test(Abstract.contains="[Ss]aid")
# dsp_chisq.test(Abstract.contains="[Cc]an")
# dsp_chisq.test(Abstract.contains="[Dd]ay")
# dsp_chisq.test(Abstract.contains="[Ss]tate")
# dsp_chisq.test(Abstract.contains="[Mm]ake")
# dsp_chisq.test(Abstract.contains="[Bb]ank")
glb_append_stop_words[["Abstract"]] <- c(NULL)
#c("fashion", "first", "intern", "make", "newyork", "report",
# "said", "share", "show", "state", "week", "year")
}
txt_corpus <- Corpus(VectorSource(txt_df))
txt_corpus <- tm_map(txt_corpus, tolower)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation)
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english")))
txt_corpus <- tm_map(txt_corpus, stemDocument)
full_freqs_DTM <- DocumentTermMatrix(txt_corpus)
print(" Full freqs:"); print(full_freqs_DTM)
full_freqs_vctr <- colSums(as.matrix(full_freqs_DTM))
names(full_freqs_vctr) <- dimnames(full_freqs_DTM)[[2]]
full_freqs_df <- as.data.frame(full_freqs_vctr)
names(full_freqs_df) <- "freq.full"
full_freqs_df$term <- rownames(full_freqs_df)
full_freqs_df <- orderBy(~ -freq.full, full_freqs_df)
sprs_freqs_DTM <- removeSparseTerms(full_freqs_DTM,
glb_sprs_thresholds[txt_var])
print(" Sparse freqs:"); print(sprs_freqs_DTM)
sprs_freqs_vctr <- colSums(as.matrix(sprs_freqs_DTM))
names(sprs_freqs_vctr) <- dimnames(sprs_freqs_DTM)[[2]]
sprs_freqs_df <- as.data.frame(sprs_freqs_vctr)
names(sprs_freqs_df) <- "freq.sprs"
sprs_freqs_df$term <- rownames(sprs_freqs_df)
sprs_freqs_df <- orderBy(~ -freq.sprs, sprs_freqs_df)
terms_freqs_df <- merge(full_freqs_df, sprs_freqs_df, all.x=TRUE)
melt_freqs_df <- orderBy(~ -value, melt(terms_freqs_df, id.var="term"))
print(ggplot(melt_freqs_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_freqs_df <- orderBy(~ -value,
melt(subset(terms_freqs_df, !is.na(freq.sprs)), id.var="term"))
print(myplot_hbar(melt_freqs_df, "term", "value",
colorcol_name="variable"))
melt_freqs_df <- orderBy(~ -value,
melt(subset(terms_freqs_df, is.na(freq.sprs)), id.var="term"))
print(myplot_hbar(head(melt_freqs_df, 10), "term", "value",
colorcol_name="variable"))
glb_corpus_lst[[txt_var]] <- txt_corpus
glb_full_DTM_lst[[txt_var]] <- full_freqs_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_freqs_DTM
}
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_entity_df) # warning otherwise
log_X_df <- log(1 + txt_X_df)
colnames(log_X_df) <- paste(colnames(txt_X_df), ".log", sep="")
#glb_entity_df <- cbind(glb_entity_df, txt_X_df)
glb_entity_df <- cbind(glb_entity_df, log_X_df)
# Create <txt_var>.has.http
glb_entity_df[, paste(txt_var_pfx, ".has.http", sep="")] <-
sapply(1:nrow(glb_entity_df),
function(row_ix) ifelse(grepl("http", glb_entity_df[row_ix, txt_var], fixed=TRUE),
1, 0))
# Create user-specified term vectors
# UniqueID == 4020, H.has.ebola
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test( Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
if (txt_var == "Headline") {
glb_entity_df[, paste(txt_var_pfx, ".has.ebola", sep="")] <-
sapply(1:nrow(glb_entity_df),
function(row_ix) ifelse(grepl("[Ee]bola", glb_entity_df[row_ix, txt_var]),
1, 0))
}
# Create <txt_var>.nwrds.log & .nwrds.unq.log
glb_entity_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + rowSums(as.matrix(glb_full_DTM_lst[[txt_var]])))
glb_entity_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(as.matrix(glb_full_DTM_lst[[txt_var]]) != 0))
# Create <txt_var>.nchrs.log
glb_entity_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_entity_df[, txt_var]))
glb_entity_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_entity_df[, txt_var]))
glb_entity_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_entity_df[, txt_var]))
# Create <txt_var>.npnct?.log
punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'", "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";", "<", "=",
">", "\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
for (punct_ix in 1:length(punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s char:", punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL
# print(results)
glb_entity_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(punct_vctr[punct_ix],
glb_entity_df[, txt_var]))
}
# print(head(glb_entity_df[glb_entity_df[, "H.npnct.log09"] > 0,
# c("UniqueID", "Headline", "A.npnct.log09")]))
# print(head(glb_entity_df[glb_entity_df[, "A.npnct.log14"] > 0,
# c("UniqueID", "Abstract", "A.npnct.log14")]))
# print(head(glb_entity_df[glb_entity_df[, "A.npnct.log21"] > 0,
# c("UniqueID", "Abstract", "A.npnct.log21")]))
# Create <txt_var>.has.year.colon
# mycount_pattern_occ("[0-9]{4}:", glb_entity_df$Headline[13:19])
glb_entity_df[, paste0(txt_var_pfx, ".has.year.colon")] <-
as.integer(0 + mycount_pattern_occ("[0-9]{4}:", glb_entity_df[, txt_var]))
# for (feat in paste(txt_var_pfx,
# c(".num.chars"), sep="")) {
# #print(myplot_box(glb_entity_df, paste0(feat, ".log"), glb_rsp_var))
# }
}
# Generate summaries
# print(summary(glb_entity_df))
# print(sapply(names(glb_entity_df), function(col) sum(is.na(glb_entity_df[, col]))))
# print(summary(glb_trnent_df))
# print(sapply(names(glb_trnent_df), function(col) sum(is.na(glb_trnent_df[, col]))))
# print(summary(glb_newent_df))
# print(sapply(names(glb_newent_df), function(col) sum(is.na(glb_newent_df[, col]))))
}## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate## [1] "Building corpus for Headline..."
## [1] " Full freqs:"
## <<DocumentTermMatrix (documents: 8402, terms: 9205)>>
## Non-/sparse entries: 44361/77296049
## Sparsity : 100%
## Maximal term length: 31
## Weighting : term frequency (tf)
## [1] " Sparse freqs:"
## <<DocumentTermMatrix (documents: 8402, terms: 10)>>
## Non-/sparse entries: 2407/81613
## Sparsity : 97%
## Maximal term length: 7
## Weighting : term frequency (tf)## Warning: Removed 6 rows containing missing values (geom_path).
## [1] "Building corpus for Snippet..."
## [1] " Full freqs:"
## <<DocumentTermMatrix (documents: 8402, terms: 13822)>>
## Non-/sparse entries: 105519/116026925
## Sparsity : 100%
## Maximal term length: 25
## Weighting : term frequency (tf)
## [1] " Sparse freqs:"
## <<DocumentTermMatrix (documents: 8402, terms: 22)>>
## Non-/sparse entries: 8657/176187
## Sparsity : 95%
## Maximal term length: 7
## Weighting : term frequency (tf)## Warning: Removed 6 rows containing missing values (geom_path).
## [1] "Building corpus for Abstract..."
## [1] " Full freqs:"
## <<DocumentTermMatrix (documents: 8402, terms: 13866)>>
## Non-/sparse entries: 105900/116396232
## Sparsity : 100%
## Maximal term length: 112
## Weighting : term frequency (tf)
## [1] " Sparse freqs:"
## <<DocumentTermMatrix (documents: 8402, terms: 22)>>
## Non-/sparse entries: 8672/176172
## Sparsity : 95%
## Maximal term length: 7
## Weighting : term frequency (tf)## Warning: Removed 6 rows containing missing values (geom_path).
## [1] "Binding DTM for Headline..."
## [1] "Binding DTM for Snippet..."
## [1] "Binding DTM for Abstract..."# Re-partition
glb_trnent_df <- subset(glb_entity_df, .src == "Train")
glb_newent_df <- subset(glb_entity_df, .src == "Test")
# print(sapply(names(glb_trnent_df), function(col) sum(is.na(glb_trnent_df[, col]))))
# print(sapply(names(glb_newent_df), function(col) sum(is.na(glb_newent_df[, col]))))
# print(myplot_scatter(glb_trnent_df, "<col1_name>", "<col2_name>", smooth=TRUE))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 6 extract.features 3 0 39.055 148.235 109.18
## 7 select.features 4 0 148.235 NA NAStep 4.0: select features
print(glb_feats_df <- myselect_features(entity_df=glb_trnent_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y exclude.as.feat
## Popular Popular 1.000000e+00 1
## A.nuppr.log A.nuppr.log -2.720962e-01 0
## S.nuppr.log S.nuppr.log -2.718459e-01 0
## WordCount.log WordCount.log 2.649604e-01 0
## WordCount WordCount 2.575265e-01 1
## S.nwrds.unq.log S.nwrds.unq.log -2.507969e-01 0
## A.nwrds.unq.log A.nwrds.unq.log -2.506012e-01 0
## S.nwrds.log S.nwrds.log -2.453541e-01 0
## A.nwrds.log A.nwrds.log -2.450733e-01 0
## S.nchrs.log S.nchrs.log -2.246930e-01 0
## A.nchrs.log A.nchrs.log -2.245488e-01 0
## H.nwrds.unq.log H.nwrds.unq.log -2.044964e-01 0
## H.nwrds.log H.nwrds.log -2.006864e-01 0
## H.nchrs.log H.nchrs.log -1.710624e-01 0
## PubDate.hour.fctr PubDate.hour.fctr 1.354368e-01 0
## H.npnct21.log H.npnct21.log 1.283641e-01 0
## H.nuppr.log H.nuppr.log -1.278085e-01 0
## A.ndgts.log A.ndgts.log -1.249484e-01 0
## S.ndgts.log S.ndgts.log -1.242046e-01 0
## H.ndgts.log H.ndgts.log -1.196633e-01 0
## PubDate.wkend PubDate.wkend 1.067288e-01 0
## A.npnct12.log A.npnct12.log -9.183870e-02 0
## S.npnct12.log S.npnct12.log -9.158156e-02 0
## H.npnct30.log H.npnct30.log -8.917338e-02 0
## S.week.log S.week.log -8.840293e-02 0
## A.week.log A.week.log -8.840293e-02 0
## S.fashion.log S.fashion.log -8.724932e-02 0
## A.fashion.log A.fashion.log -8.724932e-02 0
## H.npnct16.log H.npnct16.log -8.273237e-02 0
## H.fashion.log H.fashion.log -8.204998e-02 0
## H.has.year.colon H.has.year.colon -7.842875e-02 0
## H.week.log H.week.log -7.510522e-02 0
## H.daili.log H.daili.log -6.919298e-02 0
## A.npnct16.log A.npnct16.log -6.893301e-02 0
## S.intern.log S.intern.log -6.864274e-02 0
## A.intern.log A.intern.log -6.864274e-02 0
## S.npnct16.log S.npnct16.log -6.770952e-02 0
## H.X2015.log H.X2015.log -6.658489e-02 0
## H.report.log H.report.log -6.494810e-02 0
## H.today.log H.today.log -6.372306e-02 0
## S.npnct04.log S.npnct04.log -6.294642e-02 0
## A.npnct04.log A.npnct04.log -6.294642e-02 0
## H.day.log H.day.log -6.272898e-02 0
## S.newyork.log S.newyork.log -6.219997e-02 0
## A.newyork.log A.newyork.log -6.219997e-02 0
## H.npnct15.log H.npnct15.log -6.158577e-02 0
## A.will.log A.will.log -6.147068e-02 0
## S.will.log S.will.log -6.103349e-02 0
## S.articl.log S.articl.log -5.952055e-02 0
## A.articl.log A.articl.log -5.952055e-02 0
## H.newyork.log H.newyork.log -5.797009e-02 0
## A.time.log A.time.log -5.779371e-02 0
## S.time.log S.time.log -5.759227e-02 0
## S.npnct21.log S.npnct21.log 5.503894e-02 0
## A.npnct21.log A.npnct21.log 5.482747e-02 0
## PubDate.last10 PubDate.last10 5.398093e-02 1
## H.npnct08.log H.npnct08.log 5.375262e-02 0
## H.npnct09.log H.npnct09.log 5.375262e-02 0
## S.first.log S.first.log -5.345938e-02 0
## A.first.log A.first.log -5.345938e-02 0
## S.npnct14.log S.npnct14.log -5.332519e-02 0
## H.new.log H.new.log -5.313316e-02 0
## A.compani.log A.compani.log -5.268413e-02 0
## S.compani.log S.compani.log -5.261812e-02 0
## S.share.log S.share.log -5.138139e-02 0
## A.share.log A.share.log -5.138139e-02 0
## H.npnct04.log H.npnct04.log -5.126277e-02 0
## S.year.log S.year.log -5.094457e-02 0
## A.year.log A.year.log -5.094457e-02 0
## S.report.log S.report.log -5.032801e-02 0
## A.report.log A.report.log -5.032801e-02 0
## A.npnct14.log A.npnct14.log -4.999563e-02 0
## PubDate.last10.log PubDate.last10.log 4.931702e-02 0
## S.show.log S.show.log -4.897915e-02 0
## A.show.log A.show.log -4.897915e-02 0
## PubDate.last1.log PubDate.last1.log 4.635751e-02 0
## H.X2014.log H.X2014.log -4.620638e-02 0
## A.day.log A.day.log -4.581783e-02 0
## S.day.log S.day.log -4.555421e-02 0
## A.npnct30.log A.npnct30.log -4.373349e-02 0
## S.npnct30.log S.npnct30.log -4.370037e-02 0
## PubDate.last100 PubDate.last100 3.989229e-02 1
## PubDate.wkday.fctr PubDate.wkday.fctr -3.980129e-02 0
## A.npnct13.log A.npnct13.log -3.760012e-02 0
## S.npnct13.log S.npnct13.log -3.638891e-02 0
## PubDate.last1 PubDate.last1 3.592267e-02 1
## A.new.log A.new.log -3.524871e-02 0
## S.new.log S.new.log -3.483189e-02 0
## PubDate.minute.fctr PubDate.minute.fctr -3.407385e-02 0
## H.npnct06.log H.npnct06.log 3.190718e-02 0
## A.can.log A.can.log 3.169296e-02 0
## S.npnct01.log S.npnct01.log 3.093101e-02 0
## A.npnct01.log A.npnct01.log 3.093101e-02 0
## S.can.log S.can.log 3.077833e-02 0
## H.npnct17.log H.npnct17.log 3.039622e-02 0
## S.npnct23.log S.npnct23.log 2.760321e-02 0
## S.npnct25.log S.npnct25.log 2.760321e-02 0
## A.take.log A.take.log -2.601772e-02 0
## H.has.ebola H.has.ebola 2.588140e-02 0
## S.take.log S.take.log -2.569295e-02 0
## H.npnct14.log H.npnct14.log -2.524770e-02 0
## A.npnct15.log A.npnct15.log -2.407715e-02 0
## S.npnct06.log S.npnct06.log -2.389145e-02 0
## A.npnct06.log A.npnct06.log -2.389145e-02 0
## S.make.log S.make.log 2.334962e-02 0
## A.make.log A.make.log 2.334962e-02 0
## H.npnct01.log H.npnct01.log 2.271577e-02 0
## S.npnct15.log S.npnct15.log -2.121844e-02 0
## S.presid.log S.presid.log -2.014404e-02 0
## A.presid.log A.presid.log -2.014404e-02 0
## H.npnct02.log H.npnct02.log -2.001851e-02 0
## S.npnct22.log S.npnct22.log -1.923169e-02 0
## A.npnct22.log A.npnct22.log -1.923169e-02 0
## PubDate.month.fctr PubDate.month.fctr 1.914874e-02 1
## .rnorm .rnorm 1.756172e-02 0
## S.has.year.colon S.has.year.colon -1.755336e-02 0
## A.has.year.colon A.has.year.colon -1.755336e-02 0
## PubDate.POSIX PubDate.POSIX 1.568326e-02 1
## PubDate.zoo PubDate.zoo 1.568326e-02 1
## A.npnct23.log A.npnct23.log 1.537569e-02 0
## A.npnct25.log A.npnct25.log 1.537569e-02 0
## A.npnct02.log A.npnct02.log -1.451467e-02 0
## A.npnct18.log A.npnct18.log -1.451467e-02 0
## A.npnct20.log A.npnct20.log -1.451467e-02 0
## A.has.http A.has.http -1.359260e-02 0
## A.npnct03.log A.npnct03.log -1.359260e-02 0
## H.npnct12.log H.npnct12.log 1.333613e-02 0
## H.npnct13.log H.npnct13.log -1.305305e-02 0
## A.npnct19.log A.npnct19.log -1.271661e-02 0
## S.npnct03.log S.npnct03.log -1.240734e-02 0
## myCategory.fctr myCategory.fctr 1.234541e-02 0
## S.npnct07.log S.npnct07.log -1.214357e-02 0
## A.npnct07.log A.npnct07.log -1.214357e-02 0
## H.npnct07.log H.npnct07.log -1.201741e-02 0
## PubDate.second.fctr PubDate.second.fctr -1.187946e-02 0
## UniqueID UniqueID 1.182492e-02 1
## PubDate.date.fctr PubDate.date.fctr -1.164756e-02 0
## H.npnct05.log H.npnct05.log -9.653967e-03 0
## H.npnct03.log H.npnct03.log 9.533020e-03 0
## PubDate.last100.log PubDate.last100.log -7.663322e-03 0
## S.state.log S.state.log 7.050791e-03 0
## A.state.log A.state.log 6.668101e-03 0
## H.npnct11.log H.npnct11.log -5.547032e-03 0
## H.npnct22.log H.npnct22.log -5.547032e-03 0
## S.npnct02.log S.npnct02.log -5.547032e-03 0
## S.npnct11.log S.npnct11.log -5.547032e-03 0
## A.npnct11.log A.npnct11.log -5.547032e-03 0
## A.npnct27.log A.npnct27.log -5.547032e-03 0
## S.one.log S.one.log 4.891059e-03 0
## A.npnct09.log A.npnct09.log -4.775988e-03 0
## A.one.log A.one.log 4.368856e-03 0
## S.npnct09.log S.npnct09.log -3.986882e-03 0
## A.npnct08.log A.npnct08.log -3.258100e-03 0
## S.npnct08.log S.npnct08.log -2.413868e-03 0
## S.npnct17.log S.npnct17.log -1.587454e-03 0
## A.npnct17.log A.npnct17.log -1.587454e-03 0
## S.said.log S.said.log 3.735051e-04 0
## A.said.log A.said.log 3.735051e-04 0
## H.npnct26.log H.npnct26.log -9.890046e-19 0
## S.npnct26.log S.npnct26.log -9.890046e-19 0
## A.npnct26.log A.npnct26.log -9.890046e-19 0
## H.has.http H.has.http NA 0
## H.npnct10.log H.npnct10.log NA 0
## H.npnct18.log H.npnct18.log NA 0
## H.npnct19.log H.npnct19.log NA 0
## H.npnct20.log H.npnct20.log NA 0
## H.npnct23.log H.npnct23.log NA 0
## H.npnct24.log H.npnct24.log NA 0
## H.npnct25.log H.npnct25.log NA 0
## H.npnct27.log H.npnct27.log NA 0
## H.npnct28.log H.npnct28.log NA 0
## H.npnct29.log H.npnct29.log NA 0
## H.npnct31.log H.npnct31.log NA 0
## H.npnct32.log H.npnct32.log NA 0
## S.has.http S.has.http NA 0
## S.npnct05.log S.npnct05.log NA 0
## S.npnct10.log S.npnct10.log NA 0
## S.npnct18.log S.npnct18.log NA 0
## S.npnct19.log S.npnct19.log NA 0
## S.npnct20.log S.npnct20.log NA 0
## S.npnct24.log S.npnct24.log NA 0
## S.npnct27.log S.npnct27.log NA 0
## S.npnct28.log S.npnct28.log NA 0
## S.npnct29.log S.npnct29.log NA 0
## S.npnct31.log S.npnct31.log NA 0
## S.npnct32.log S.npnct32.log NA 0
## A.npnct05.log A.npnct05.log NA 0
## A.npnct10.log A.npnct10.log NA 0
## A.npnct24.log A.npnct24.log NA 0
## A.npnct28.log A.npnct28.log NA 0
## A.npnct29.log A.npnct29.log NA 0
## A.npnct31.log A.npnct31.log NA 0
## A.npnct32.log A.npnct32.log NA 0
## PubDate.year.fctr PubDate.year.fctr NA 0
## cor.y.abs
## Popular 1.000000e+00
## A.nuppr.log 2.720962e-01
## S.nuppr.log 2.718459e-01
## WordCount.log 2.649604e-01
## WordCount 2.575265e-01
## S.nwrds.unq.log 2.507969e-01
## A.nwrds.unq.log 2.506012e-01
## S.nwrds.log 2.453541e-01
## A.nwrds.log 2.450733e-01
## S.nchrs.log 2.246930e-01
## A.nchrs.log 2.245488e-01
## H.nwrds.unq.log 2.044964e-01
## H.nwrds.log 2.006864e-01
## H.nchrs.log 1.710624e-01
## PubDate.hour.fctr 1.354368e-01
## H.npnct21.log 1.283641e-01
## H.nuppr.log 1.278085e-01
## A.ndgts.log 1.249484e-01
## S.ndgts.log 1.242046e-01
## H.ndgts.log 1.196633e-01
## PubDate.wkend 1.067288e-01
## A.npnct12.log 9.183870e-02
## S.npnct12.log 9.158156e-02
## H.npnct30.log 8.917338e-02
## S.week.log 8.840293e-02
## A.week.log 8.840293e-02
## S.fashion.log 8.724932e-02
## A.fashion.log 8.724932e-02
## H.npnct16.log 8.273237e-02
## H.fashion.log 8.204998e-02
## H.has.year.colon 7.842875e-02
## H.week.log 7.510522e-02
## H.daili.log 6.919298e-02
## A.npnct16.log 6.893301e-02
## S.intern.log 6.864274e-02
## A.intern.log 6.864274e-02
## S.npnct16.log 6.770952e-02
## H.X2015.log 6.658489e-02
## H.report.log 6.494810e-02
## H.today.log 6.372306e-02
## S.npnct04.log 6.294642e-02
## A.npnct04.log 6.294642e-02
## H.day.log 6.272898e-02
## S.newyork.log 6.219997e-02
## A.newyork.log 6.219997e-02
## H.npnct15.log 6.158577e-02
## A.will.log 6.147068e-02
## S.will.log 6.103349e-02
## S.articl.log 5.952055e-02
## A.articl.log 5.952055e-02
## H.newyork.log 5.797009e-02
## A.time.log 5.779371e-02
## S.time.log 5.759227e-02
## S.npnct21.log 5.503894e-02
## A.npnct21.log 5.482747e-02
## PubDate.last10 5.398093e-02
## H.npnct08.log 5.375262e-02
## H.npnct09.log 5.375262e-02
## S.first.log 5.345938e-02
## A.first.log 5.345938e-02
## S.npnct14.log 5.332519e-02
## H.new.log 5.313316e-02
## A.compani.log 5.268413e-02
## S.compani.log 5.261812e-02
## S.share.log 5.138139e-02
## A.share.log 5.138139e-02
## H.npnct04.log 5.126277e-02
## S.year.log 5.094457e-02
## A.year.log 5.094457e-02
## S.report.log 5.032801e-02
## A.report.log 5.032801e-02
## A.npnct14.log 4.999563e-02
## PubDate.last10.log 4.931702e-02
## S.show.log 4.897915e-02
## A.show.log 4.897915e-02
## PubDate.last1.log 4.635751e-02
## H.X2014.log 4.620638e-02
## A.day.log 4.581783e-02
## S.day.log 4.555421e-02
## A.npnct30.log 4.373349e-02
## S.npnct30.log 4.370037e-02
## PubDate.last100 3.989229e-02
## PubDate.wkday.fctr 3.980129e-02
## A.npnct13.log 3.760012e-02
## S.npnct13.log 3.638891e-02
## PubDate.last1 3.592267e-02
## A.new.log 3.524871e-02
## S.new.log 3.483189e-02
## PubDate.minute.fctr 3.407385e-02
## H.npnct06.log 3.190718e-02
## A.can.log 3.169296e-02
## S.npnct01.log 3.093101e-02
## A.npnct01.log 3.093101e-02
## S.can.log 3.077833e-02
## H.npnct17.log 3.039622e-02
## S.npnct23.log 2.760321e-02
## S.npnct25.log 2.760321e-02
## A.take.log 2.601772e-02
## H.has.ebola 2.588140e-02
## S.take.log 2.569295e-02
## H.npnct14.log 2.524770e-02
## A.npnct15.log 2.407715e-02
## S.npnct06.log 2.389145e-02
## A.npnct06.log 2.389145e-02
## S.make.log 2.334962e-02
## A.make.log 2.334962e-02
## H.npnct01.log 2.271577e-02
## S.npnct15.log 2.121844e-02
## S.presid.log 2.014404e-02
## A.presid.log 2.014404e-02
## H.npnct02.log 2.001851e-02
## S.npnct22.log 1.923169e-02
## A.npnct22.log 1.923169e-02
## PubDate.month.fctr 1.914874e-02
## .rnorm 1.756172e-02
## S.has.year.colon 1.755336e-02
## A.has.year.colon 1.755336e-02
## PubDate.POSIX 1.568326e-02
## PubDate.zoo 1.568326e-02
## A.npnct23.log 1.537569e-02
## A.npnct25.log 1.537569e-02
## A.npnct02.log 1.451467e-02
## A.npnct18.log 1.451467e-02
## A.npnct20.log 1.451467e-02
## A.has.http 1.359260e-02
## A.npnct03.log 1.359260e-02
## H.npnct12.log 1.333613e-02
## H.npnct13.log 1.305305e-02
## A.npnct19.log 1.271661e-02
## S.npnct03.log 1.240734e-02
## myCategory.fctr 1.234541e-02
## S.npnct07.log 1.214357e-02
## A.npnct07.log 1.214357e-02
## H.npnct07.log 1.201741e-02
## PubDate.second.fctr 1.187946e-02
## UniqueID 1.182492e-02
## PubDate.date.fctr 1.164756e-02
## H.npnct05.log 9.653967e-03
## H.npnct03.log 9.533020e-03
## PubDate.last100.log 7.663322e-03
## S.state.log 7.050791e-03
## A.state.log 6.668101e-03
## H.npnct11.log 5.547032e-03
## H.npnct22.log 5.547032e-03
## S.npnct02.log 5.547032e-03
## S.npnct11.log 5.547032e-03
## A.npnct11.log 5.547032e-03
## A.npnct27.log 5.547032e-03
## S.one.log 4.891059e-03
## A.npnct09.log 4.775988e-03
## A.one.log 4.368856e-03
## S.npnct09.log 3.986882e-03
## A.npnct08.log 3.258100e-03
## S.npnct08.log 2.413868e-03
## S.npnct17.log 1.587454e-03
## A.npnct17.log 1.587454e-03
## S.said.log 3.735051e-04
## A.said.log 3.735051e-04
## H.npnct26.log 9.890046e-19
## S.npnct26.log 9.890046e-19
## A.npnct26.log 9.890046e-19
## H.has.http NA
## H.npnct10.log NA
## H.npnct18.log NA
## H.npnct19.log NA
## H.npnct20.log NA
## H.npnct23.log NA
## H.npnct24.log NA
## H.npnct25.log NA
## H.npnct27.log NA
## H.npnct28.log NA
## H.npnct29.log NA
## H.npnct31.log NA
## H.npnct32.log NA
## S.has.http NA
## S.npnct05.log NA
## S.npnct10.log NA
## S.npnct18.log NA
## S.npnct19.log NA
## S.npnct20.log NA
## S.npnct24.log NA
## S.npnct27.log NA
## S.npnct28.log NA
## S.npnct29.log NA
## S.npnct31.log NA
## S.npnct32.log NA
## A.npnct05.log NA
## A.npnct10.log NA
## A.npnct24.log NA
## A.npnct28.log NA
## A.npnct29.log NA
## A.npnct31.log NA
## A.npnct32.log NA
## PubDate.year.fctr NAprint(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, entity_df=glb_trnent_df,
rsp_var=glb_rsp_var)))## Loading required package: caret
##
## Attaching package: 'caret'
##
## The following object is masked from 'package:survival':
##
## cluster## [1] "cor(A.articl.log, S.articl.log)=1.0000"
## [1] "cor(Popular.fctr, A.articl.log)=-0.0595"
## [1] "cor(Popular.fctr, S.articl.log)=-0.0595"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.articl.log as highly correlated with
## A.articl.log## [1] "cor(A.fashion.log, S.fashion.log)=1.0000"
## [1] "cor(Popular.fctr, A.fashion.log)=-0.0872"
## [1] "cor(Popular.fctr, S.fashion.log)=-0.0872"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.fashion.log as highly correlated with
## A.fashion.log## [1] "cor(A.first.log, S.first.log)=1.0000"
## [1] "cor(Popular.fctr, A.first.log)=-0.0535"
## [1] "cor(Popular.fctr, S.first.log)=-0.0535"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.first.log as highly correlated with
## A.first.log## [1] "cor(A.intern.log, S.intern.log)=1.0000"
## [1] "cor(Popular.fctr, A.intern.log)=-0.0686"
## [1] "cor(Popular.fctr, S.intern.log)=-0.0686"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.intern.log as highly correlated with
## A.intern.log## [1] "cor(A.make.log, S.make.log)=1.0000"
## [1] "cor(Popular.fctr, A.make.log)=0.0233"
## [1] "cor(Popular.fctr, S.make.log)=0.0233"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.make.log as highly correlated with
## A.make.log## [1] "cor(A.newyork.log, S.newyork.log)=1.0000"
## [1] "cor(Popular.fctr, A.newyork.log)=-0.0622"
## [1] "cor(Popular.fctr, S.newyork.log)=-0.0622"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.newyork.log as highly correlated with
## A.newyork.log## [1] "cor(A.npnct01.log, S.npnct01.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct01.log)=0.0309"
## [1] "cor(Popular.fctr, S.npnct01.log)=0.0309"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.npnct01.log as highly correlated with
## A.npnct01.log## [1] "cor(A.npnct04.log, S.npnct04.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct04.log)=-0.0629"
## [1] "cor(Popular.fctr, S.npnct04.log)=-0.0629"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.npnct04.log as highly correlated with
## A.npnct04.log## [1] "cor(A.npnct06.log, S.npnct06.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct06.log)=-0.0239"
## [1] "cor(Popular.fctr, S.npnct06.log)=-0.0239"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.npnct06.log as highly correlated with
## A.npnct06.log## [1] "cor(A.npnct22.log, S.npnct22.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct22.log)=-0.0192"
## [1] "cor(Popular.fctr, S.npnct22.log)=-0.0192"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.npnct22.log as highly correlated with
## A.npnct22.log## [1] "cor(A.presid.log, S.presid.log)=1.0000"
## [1] "cor(Popular.fctr, A.presid.log)=-0.0201"
## [1] "cor(Popular.fctr, S.presid.log)=-0.0201"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.presid.log as highly correlated with
## A.presid.log## [1] "cor(A.report.log, S.report.log)=1.0000"
## [1] "cor(Popular.fctr, A.report.log)=-0.0503"
## [1] "cor(Popular.fctr, S.report.log)=-0.0503"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.report.log as highly correlated with
## A.report.log## [1] "cor(A.share.log, S.share.log)=1.0000"
## [1] "cor(Popular.fctr, A.share.log)=-0.0514"
## [1] "cor(Popular.fctr, S.share.log)=-0.0514"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.share.log as highly correlated with
## A.share.log## [1] "cor(A.show.log, S.show.log)=1.0000"
## [1] "cor(Popular.fctr, A.show.log)=-0.0490"
## [1] "cor(Popular.fctr, S.show.log)=-0.0490"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.show.log as highly correlated with
## A.show.log## [1] "cor(A.week.log, S.week.log)=1.0000"
## [1] "cor(Popular.fctr, A.week.log)=-0.0884"
## [1] "cor(Popular.fctr, S.week.log)=-0.0884"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.week.log as highly correlated with
## A.week.log## [1] "cor(A.year.log, S.year.log)=1.0000"
## [1] "cor(Popular.fctr, A.year.log)=-0.0509"
## [1] "cor(Popular.fctr, S.year.log)=-0.0509"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.year.log as highly correlated with
## A.year.log## [1] "cor(H.npnct08.log, H.npnct09.log)=1.0000"
## [1] "cor(Popular.fctr, H.npnct08.log)=0.0538"
## [1] "cor(Popular.fctr, H.npnct09.log)=0.0538"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified H.npnct09.log as highly correlated with
## H.npnct08.log## [1] "cor(S.npnct23.log, S.npnct25.log)=1.0000"
## [1] "cor(Popular.fctr, S.npnct23.log)=0.0276"
## [1] "cor(Popular.fctr, S.npnct25.log)=0.0276"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.npnct25.log as highly correlated with
## S.npnct23.log## [1] "cor(A.npnct12.log, S.npnct12.log)=0.9997"
## [1] "cor(Popular.fctr, A.npnct12.log)=-0.0918"
## [1] "cor(Popular.fctr, S.npnct12.log)=-0.0916"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.npnct12.log as highly correlated with
## A.npnct12.log## [1] "cor(A.compani.log, S.compani.log)=0.9995"
## [1] "cor(Popular.fctr, A.compani.log)=-0.0527"
## [1] "cor(Popular.fctr, S.compani.log)=-0.0526"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.compani.log as highly correlated with
## A.compani.log## [1] "cor(A.can.log, S.can.log)=0.9993"
## [1] "cor(Popular.fctr, A.can.log)=0.0317"
## [1] "cor(Popular.fctr, S.can.log)=0.0308"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.can.log as highly correlated with A.can.log## [1] "cor(A.nuppr.log, S.nuppr.log)=0.9991"
## [1] "cor(Popular.fctr, A.nuppr.log)=-0.2721"
## [1] "cor(Popular.fctr, S.nuppr.log)=-0.2718"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.nuppr.log as highly correlated with
## A.nuppr.log## [1] "cor(A.time.log, S.time.log)=0.9990"
## [1] "cor(Popular.fctr, A.time.log)=-0.0578"
## [1] "cor(Popular.fctr, S.time.log)=-0.0576"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.time.log as highly correlated with
## A.time.log## [1] "cor(A.npnct30.log, S.npnct30.log)=0.9989"
## [1] "cor(Popular.fctr, A.npnct30.log)=-0.0437"
## [1] "cor(Popular.fctr, S.npnct30.log)=-0.0437"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.npnct30.log as highly correlated with
## A.npnct30.log## [1] "cor(A.nwrds.unq.log, S.nwrds.unq.log)=0.9989"
## [1] "cor(Popular.fctr, A.nwrds.unq.log)=-0.2506"
## [1] "cor(Popular.fctr, S.nwrds.unq.log)=-0.2508"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified A.nwrds.unq.log as highly correlated with
## S.nwrds.unq.log## [1] "cor(A.nwrds.log, S.nwrds.log)=0.9988"
## [1] "cor(Popular.fctr, A.nwrds.log)=-0.2451"
## [1] "cor(Popular.fctr, S.nwrds.log)=-0.2454"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified A.nwrds.log as highly correlated with
## S.nwrds.log## [1] "cor(A.nchrs.log, S.nchrs.log)=0.9986"
## [1] "cor(Popular.fctr, A.nchrs.log)=-0.2245"
## [1] "cor(Popular.fctr, S.nchrs.log)=-0.2247"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified A.nchrs.log as highly correlated with
## S.nchrs.log## [1] "cor(A.new.log, S.new.log)=0.9982"
## [1] "cor(Popular.fctr, A.new.log)=-0.0352"
## [1] "cor(Popular.fctr, S.new.log)=-0.0348"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.new.log as highly correlated with A.new.log## [1] "cor(A.day.log, S.day.log)=0.9981"
## [1] "cor(Popular.fctr, A.day.log)=-0.0458"
## [1] "cor(Popular.fctr, S.day.log)=-0.0456"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.day.log as highly correlated with A.day.log## [1] "cor(A.will.log, S.will.log)=0.9979"
## [1] "cor(Popular.fctr, A.will.log)=-0.0615"
## [1] "cor(Popular.fctr, S.will.log)=-0.0610"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.will.log as highly correlated with
## A.will.log## [1] "cor(A.take.log, S.take.log)=0.9976"
## [1] "cor(Popular.fctr, A.take.log)=-0.0260"
## [1] "cor(Popular.fctr, S.take.log)=-0.0257"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.take.log as highly correlated with
## A.take.log## [1] "cor(H.nwrds.log, H.nwrds.unq.log)=0.9967"
## [1] "cor(Popular.fctr, H.nwrds.log)=-0.2007"
## [1] "cor(Popular.fctr, H.nwrds.unq.log)=-0.2045"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified H.nwrds.log as highly correlated with
## H.nwrds.unq.log## [1] "cor(A.npnct21.log, S.npnct21.log)=0.9957"
## [1] "cor(Popular.fctr, A.npnct21.log)=0.0548"
## [1] "cor(Popular.fctr, S.npnct21.log)=0.0550"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified A.npnct21.log as highly correlated with
## S.npnct21.log## [1] "cor(A.ndgts.log, S.ndgts.log)=0.9955"
## [1] "cor(Popular.fctr, A.ndgts.log)=-0.1249"
## [1] "cor(Popular.fctr, S.ndgts.log)=-0.1242"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.ndgts.log as highly correlated with
## A.ndgts.log## [1] "cor(S.nwrds.log, S.nwrds.unq.log)=0.9954"
## [1] "cor(Popular.fctr, S.nwrds.log)=-0.2454"
## [1] "cor(Popular.fctr, S.nwrds.unq.log)=-0.2508"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.nwrds.log as highly correlated with
## S.nwrds.unq.log## [1] "cor(A.npnct13.log, S.npnct13.log)=0.9935"
## [1] "cor(Popular.fctr, A.npnct13.log)=-0.0376"
## [1] "cor(Popular.fctr, S.npnct13.log)=-0.0364"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.npnct13.log as highly correlated with
## A.npnct13.log## [1] "cor(A.npnct16.log, S.npnct16.log)=0.9917"
## [1] "cor(Popular.fctr, A.npnct16.log)=-0.0689"
## [1] "cor(Popular.fctr, S.npnct16.log)=-0.0677"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.npnct16.log as highly correlated with
## A.npnct16.log## [1] "cor(A.npnct14.log, S.npnct14.log)=0.9795"
## [1] "cor(Popular.fctr, A.npnct14.log)=-0.0500"
## [1] "cor(Popular.fctr, S.npnct14.log)=-0.0533"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified A.npnct14.log as highly correlated with
## S.npnct14.log## [1] "cor(S.nchrs.log, S.nwrds.unq.log)=0.9543"
## [1] "cor(Popular.fctr, S.nchrs.log)=-0.2247"
## [1] "cor(Popular.fctr, S.nwrds.unq.log)=-0.2508"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified S.nchrs.log as highly correlated with
## S.nwrds.unq.log## [1] "cor(H.nchrs.log, H.nwrds.unq.log)=0.8881"
## [1] "cor(Popular.fctr, H.nchrs.log)=-0.1711"
## [1] "cor(Popular.fctr, H.nwrds.unq.log)=-0.2045"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified H.nchrs.log as highly correlated with
## H.nwrds.unq.log## [1] "cor(H.npnct15.log, H.X2015.log)=0.8780"
## [1] "cor(Popular.fctr, H.npnct15.log)=-0.0616"
## [1] "cor(Popular.fctr, H.X2015.log)=-0.0666"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified H.npnct15.log as highly correlated with
## H.X2015.log## [1] "cor(H.nuppr.log, H.nwrds.unq.log)=0.8288"
## [1] "cor(Popular.fctr, H.nuppr.log)=-0.1278"
## [1] "cor(Popular.fctr, H.nwrds.unq.log)=-0.2045"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified H.nuppr.log as highly correlated with
## H.nwrds.unq.log## [1] "cor(H.npnct06.log, H.npnct17.log)=0.8106"
## [1] "cor(Popular.fctr, H.npnct06.log)=0.0319"
## [1] "cor(Popular.fctr, H.npnct17.log)=0.0304"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified H.npnct17.log as highly correlated with
## H.npnct06.log## [1] "cor(A.intern.log, H.has.year.colon)=0.7757"
## [1] "cor(Popular.fctr, A.intern.log)=-0.0686"
## [1] "cor(Popular.fctr, H.has.year.colon)=-0.0784"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified A.intern.log as highly correlated with
## H.has.year.colon## [1] "cor(H.fashion.log, H.week.log)=0.7658"
## [1] "cor(Popular.fctr, H.fashion.log)=-0.0820"
## [1] "cor(Popular.fctr, H.week.log)=-0.0751"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified H.week.log as highly correlated with
## H.fashion.log## id cor.y exclude.as.feat
## Popular Popular 1.000000e+00 1
## WordCount.log WordCount.log 2.649604e-01 0
## WordCount WordCount 2.575265e-01 1
## PubDate.hour.fctr PubDate.hour.fctr 1.354368e-01 0
## H.npnct21.log H.npnct21.log 1.283641e-01 0
## PubDate.wkend PubDate.wkend 1.067288e-01 0
## S.npnct21.log S.npnct21.log 5.503894e-02 0
## A.npnct21.log A.npnct21.log 5.482747e-02 0
## PubDate.last10 PubDate.last10 5.398093e-02 1
## H.npnct08.log H.npnct08.log 5.375262e-02 0
## H.npnct09.log H.npnct09.log 5.375262e-02 0
## PubDate.last10.log PubDate.last10.log 4.931702e-02 0
## PubDate.last1.log PubDate.last1.log 4.635751e-02 0
## PubDate.last100 PubDate.last100 3.989229e-02 1
## PubDate.last1 PubDate.last1 3.592267e-02 1
## H.npnct06.log H.npnct06.log 3.190718e-02 0
## A.can.log A.can.log 3.169296e-02 0
## A.npnct01.log A.npnct01.log 3.093101e-02 0
## S.npnct01.log S.npnct01.log 3.093101e-02 0
## S.can.log S.can.log 3.077833e-02 0
## H.npnct17.log H.npnct17.log 3.039622e-02 0
## S.npnct23.log S.npnct23.log 2.760321e-02 0
## S.npnct25.log S.npnct25.log 2.760321e-02 0
## H.has.ebola H.has.ebola 2.588140e-02 0
## A.make.log A.make.log 2.334962e-02 0
## S.make.log S.make.log 2.334962e-02 0
## H.npnct01.log H.npnct01.log 2.271577e-02 0
## PubDate.month.fctr PubDate.month.fctr 1.914874e-02 1
## .rnorm .rnorm 1.756172e-02 0
## PubDate.POSIX PubDate.POSIX 1.568326e-02 1
## PubDate.zoo PubDate.zoo 1.568326e-02 1
## A.npnct23.log A.npnct23.log 1.537569e-02 0
## A.npnct25.log A.npnct25.log 1.537569e-02 0
## H.npnct12.log H.npnct12.log 1.333613e-02 0
## myCategory.fctr myCategory.fctr 1.234541e-02 0
## UniqueID UniqueID 1.182492e-02 1
## H.npnct03.log H.npnct03.log 9.533020e-03 0
## S.state.log S.state.log 7.050791e-03 0
## A.state.log A.state.log 6.668101e-03 0
## S.one.log S.one.log 4.891059e-03 0
## A.one.log A.one.log 4.368856e-03 0
## A.said.log A.said.log 3.735051e-04 0
## S.said.log S.said.log 3.735051e-04 0
## A.npnct26.log A.npnct26.log -9.890046e-19 0
## H.npnct26.log H.npnct26.log -9.890046e-19 0
## S.npnct26.log S.npnct26.log -9.890046e-19 0
## A.npnct17.log A.npnct17.log -1.587454e-03 0
## S.npnct17.log S.npnct17.log -1.587454e-03 0
## S.npnct08.log S.npnct08.log -2.413868e-03 0
## A.npnct08.log A.npnct08.log -3.258100e-03 0
## S.npnct09.log S.npnct09.log -3.986882e-03 0
## A.npnct09.log A.npnct09.log -4.775988e-03 0
## A.npnct27.log A.npnct27.log -5.547032e-03 0
## A.npnct11.log A.npnct11.log -5.547032e-03 0
## H.npnct11.log H.npnct11.log -5.547032e-03 0
## H.npnct22.log H.npnct22.log -5.547032e-03 0
## S.npnct02.log S.npnct02.log -5.547032e-03 0
## S.npnct11.log S.npnct11.log -5.547032e-03 0
## PubDate.last100.log PubDate.last100.log -7.663322e-03 0
## H.npnct05.log H.npnct05.log -9.653967e-03 0
## PubDate.date.fctr PubDate.date.fctr -1.164756e-02 0
## PubDate.second.fctr PubDate.second.fctr -1.187946e-02 0
## H.npnct07.log H.npnct07.log -1.201741e-02 0
## A.npnct07.log A.npnct07.log -1.214357e-02 0
## S.npnct07.log S.npnct07.log -1.214357e-02 0
## S.npnct03.log S.npnct03.log -1.240734e-02 0
## A.npnct19.log A.npnct19.log -1.271661e-02 0
## H.npnct13.log H.npnct13.log -1.305305e-02 0
## A.has.http A.has.http -1.359260e-02 0
## A.npnct03.log A.npnct03.log -1.359260e-02 0
## A.npnct02.log A.npnct02.log -1.451467e-02 0
## A.npnct18.log A.npnct18.log -1.451467e-02 0
## A.npnct20.log A.npnct20.log -1.451467e-02 0
## A.has.year.colon A.has.year.colon -1.755336e-02 0
## S.has.year.colon S.has.year.colon -1.755336e-02 0
## A.npnct22.log A.npnct22.log -1.923169e-02 0
## S.npnct22.log S.npnct22.log -1.923169e-02 0
## H.npnct02.log H.npnct02.log -2.001851e-02 0
## A.presid.log A.presid.log -2.014404e-02 0
## S.presid.log S.presid.log -2.014404e-02 0
## S.npnct15.log S.npnct15.log -2.121844e-02 0
## A.npnct06.log A.npnct06.log -2.389145e-02 0
## S.npnct06.log S.npnct06.log -2.389145e-02 0
## A.npnct15.log A.npnct15.log -2.407715e-02 0
## H.npnct14.log H.npnct14.log -2.524770e-02 0
## S.take.log S.take.log -2.569295e-02 0
## A.take.log A.take.log -2.601772e-02 0
## PubDate.minute.fctr PubDate.minute.fctr -3.407385e-02 0
## S.new.log S.new.log -3.483189e-02 0
## A.new.log A.new.log -3.524871e-02 0
## S.npnct13.log S.npnct13.log -3.638891e-02 0
## A.npnct13.log A.npnct13.log -3.760012e-02 0
## PubDate.wkday.fctr PubDate.wkday.fctr -3.980129e-02 0
## S.npnct30.log S.npnct30.log -4.370037e-02 0
## A.npnct30.log A.npnct30.log -4.373349e-02 0
## S.day.log S.day.log -4.555421e-02 0
## A.day.log A.day.log -4.581783e-02 0
## H.X2014.log H.X2014.log -4.620638e-02 0
## A.show.log A.show.log -4.897915e-02 0
## S.show.log S.show.log -4.897915e-02 0
## A.npnct14.log A.npnct14.log -4.999563e-02 0
## A.report.log A.report.log -5.032801e-02 0
## S.report.log S.report.log -5.032801e-02 0
## A.year.log A.year.log -5.094457e-02 0
## S.year.log S.year.log -5.094457e-02 0
## H.npnct04.log H.npnct04.log -5.126277e-02 0
## A.share.log A.share.log -5.138139e-02 0
## S.share.log S.share.log -5.138139e-02 0
## S.compani.log S.compani.log -5.261812e-02 0
## A.compani.log A.compani.log -5.268413e-02 0
## H.new.log H.new.log -5.313316e-02 0
## S.npnct14.log S.npnct14.log -5.332519e-02 0
## A.first.log A.first.log -5.345938e-02 0
## S.first.log S.first.log -5.345938e-02 0
## S.time.log S.time.log -5.759227e-02 0
## A.time.log A.time.log -5.779371e-02 0
## H.newyork.log H.newyork.log -5.797009e-02 0
## A.articl.log A.articl.log -5.952055e-02 0
## S.articl.log S.articl.log -5.952055e-02 0
## S.will.log S.will.log -6.103349e-02 0
## A.will.log A.will.log -6.147068e-02 0
## H.npnct15.log H.npnct15.log -6.158577e-02 0
## A.newyork.log A.newyork.log -6.219997e-02 0
## S.newyork.log S.newyork.log -6.219997e-02 0
## H.day.log H.day.log -6.272898e-02 0
## A.npnct04.log A.npnct04.log -6.294642e-02 0
## S.npnct04.log S.npnct04.log -6.294642e-02 0
## H.today.log H.today.log -6.372306e-02 0
## H.report.log H.report.log -6.494810e-02 0
## H.X2015.log H.X2015.log -6.658489e-02 0
## S.npnct16.log S.npnct16.log -6.770952e-02 0
## A.intern.log A.intern.log -6.864274e-02 0
## S.intern.log S.intern.log -6.864274e-02 0
## A.npnct16.log A.npnct16.log -6.893301e-02 0
## H.daili.log H.daili.log -6.919298e-02 0
## H.week.log H.week.log -7.510522e-02 0
## H.has.year.colon H.has.year.colon -7.842875e-02 0
## H.fashion.log H.fashion.log -8.204998e-02 0
## H.npnct16.log H.npnct16.log -8.273237e-02 0
## A.fashion.log A.fashion.log -8.724932e-02 0
## S.fashion.log S.fashion.log -8.724932e-02 0
## A.week.log A.week.log -8.840293e-02 0
## S.week.log S.week.log -8.840293e-02 0
## H.npnct30.log H.npnct30.log -8.917338e-02 0
## S.npnct12.log S.npnct12.log -9.158156e-02 0
## A.npnct12.log A.npnct12.log -9.183870e-02 0
## H.ndgts.log H.ndgts.log -1.196633e-01 0
## S.ndgts.log S.ndgts.log -1.242046e-01 0
## A.ndgts.log A.ndgts.log -1.249484e-01 0
## H.nuppr.log H.nuppr.log -1.278085e-01 0
## H.nchrs.log H.nchrs.log -1.710624e-01 0
## H.nwrds.log H.nwrds.log -2.006864e-01 0
## H.nwrds.unq.log H.nwrds.unq.log -2.044964e-01 0
## A.nchrs.log A.nchrs.log -2.245488e-01 0
## S.nchrs.log S.nchrs.log -2.246930e-01 0
## A.nwrds.log A.nwrds.log -2.450733e-01 0
## S.nwrds.log S.nwrds.log -2.453541e-01 0
## A.nwrds.unq.log A.nwrds.unq.log -2.506012e-01 0
## S.nwrds.unq.log S.nwrds.unq.log -2.507969e-01 0
## S.nuppr.log S.nuppr.log -2.718459e-01 0
## A.nuppr.log A.nuppr.log -2.720962e-01 0
## A.npnct05.log A.npnct05.log NA 0
## A.npnct10.log A.npnct10.log NA 0
## A.npnct24.log A.npnct24.log NA 0
## A.npnct28.log A.npnct28.log NA 0
## A.npnct29.log A.npnct29.log NA 0
## A.npnct31.log A.npnct31.log NA 0
## A.npnct32.log A.npnct32.log NA 0
## H.has.http H.has.http NA 0
## H.npnct10.log H.npnct10.log NA 0
## H.npnct18.log H.npnct18.log NA 0
## H.npnct19.log H.npnct19.log NA 0
## H.npnct20.log H.npnct20.log NA 0
## H.npnct23.log H.npnct23.log NA 0
## H.npnct24.log H.npnct24.log NA 0
## H.npnct25.log H.npnct25.log NA 0
## H.npnct27.log H.npnct27.log NA 0
## H.npnct28.log H.npnct28.log NA 0
## H.npnct29.log H.npnct29.log NA 0
## H.npnct31.log H.npnct31.log NA 0
## H.npnct32.log H.npnct32.log NA 0
## PubDate.year.fctr PubDate.year.fctr NA 0
## S.has.http S.has.http NA 0
## S.npnct05.log S.npnct05.log NA 0
## S.npnct10.log S.npnct10.log NA 0
## S.npnct18.log S.npnct18.log NA 0
## S.npnct19.log S.npnct19.log NA 0
## S.npnct20.log S.npnct20.log NA 0
## S.npnct24.log S.npnct24.log NA 0
## S.npnct27.log S.npnct27.log NA 0
## S.npnct28.log S.npnct28.log NA 0
## S.npnct29.log S.npnct29.log NA 0
## S.npnct31.log S.npnct31.log NA 0
## S.npnct32.log S.npnct32.log NA 0
## cor.y.abs cor.high.X freqRatio percentUnique
## Popular 1.000000e+00 <NA> 4.976212 0.03061849
## WordCount.log 2.649604e-01 <NA> 1.266667 24.15799143
## WordCount 2.575265e-01 <NA> 2.315789 24.15799143
## PubDate.hour.fctr 1.354368e-01 <NA> 1.835040 0.04592774
## H.npnct21.log 1.283641e-01 <NA> 14.995098 0.06123699
## PubDate.wkend 1.067288e-01 <NA> 9.095827 0.03061849
## S.npnct21.log 5.503894e-02 A.npnct21.log 12.862366 0.07654623
## A.npnct21.log 5.482747e-02 <NA> 12.798715 0.07654623
## PubDate.last10 5.398093e-02 <NA> 1.666667 79.05695040
## H.npnct08.log 5.375262e-02 H.npnct09.log 111.620690 0.03061849
## H.npnct09.log 5.375262e-02 <NA> 111.620690 0.03061849
## PubDate.last10.log 4.931702e-02 <NA> 1.666667 79.05695040
## PubDate.last1.log 4.635751e-02 <NA> 1.142857 36.49724434
## PubDate.last100 3.989229e-02 <NA> 25.000000 92.52908757
## PubDate.last1 3.592267e-02 <NA> 1.142857 36.49724434
## H.npnct06.log 3.190718e-02 H.npnct17.log 68.935484 0.06123699
## A.can.log 3.169296e-02 S.can.log 26.166667 0.04592774
## A.npnct01.log 3.093101e-02 S.npnct01.log 309.952381 0.06123699
## S.npnct01.log 3.093101e-02 <NA> 309.952381 0.06123699
## S.can.log 3.077833e-02 <NA> 26.058091 0.04592774
## H.npnct17.log 3.039622e-02 <NA> 96.104478 0.06123699
## S.npnct23.log 2.760321e-02 S.npnct25.log 6531.000000 0.03061849
## S.npnct25.log 2.760321e-02 <NA> 6531.000000 0.03061849
## H.has.ebola 2.588140e-02 <NA> 73.227273 0.03061849
## A.make.log 2.334962e-02 S.make.log 27.378261 0.04592774
## S.make.log 2.334962e-02 <NA> 27.378261 0.04592774
## H.npnct01.log 2.271577e-02 <NA> 282.913043 0.04592774
## PubDate.month.fctr 1.914874e-02 <NA> 1.017514 0.04592774
## .rnorm 1.756172e-02 <NA> 1.000000 100.00000000
## PubDate.POSIX 1.568326e-02 <NA> 1.000000 99.86221678
## PubDate.zoo 1.568326e-02 <NA> 1.000000 99.86221678
## A.npnct23.log 1.537569e-02 <NA> 3264.500000 0.04592774
## A.npnct25.log 1.537569e-02 <NA> 3264.500000 0.04592774
## H.npnct12.log 1.333613e-02 <NA> 4.937442 0.07654623
## myCategory.fctr 1.234541e-02 <NA> 1.337185 0.30618494
## UniqueID 1.182492e-02 <NA> 1.000000 100.00000000
## H.npnct03.log 9.533020e-03 <NA> 2176.333333 0.03061849
## S.state.log 7.050791e-03 <NA> 30.655340 0.04592774
## A.state.log 6.668101e-03 <NA> 30.502415 0.04592774
## S.one.log 4.891059e-03 <NA> 22.777372 0.04592774
## A.one.log 4.368856e-03 <NA> 22.773723 0.04592774
## A.said.log 3.735051e-04 <NA> 25.212851 0.04592774
## S.said.log 3.735051e-04 <NA> 25.212851 0.04592774
## A.npnct26.log 9.890046e-19 <NA> 0.000000 0.01530925
## H.npnct26.log 9.890046e-19 <NA> 0.000000 0.01530925
## S.npnct26.log 9.890046e-19 <NA> 0.000000 0.01530925
## A.npnct17.log 1.587454e-03 <NA> 434.133333 0.04592774
## S.npnct17.log 1.587454e-03 <NA> 434.133333 0.04592774
## S.npnct08.log 2.413868e-03 <NA> 175.513514 0.04592774
## A.npnct08.log 3.258100e-03 <NA> 170.868421 0.04592774
## S.npnct09.log 3.986882e-03 <NA> 175.486486 0.06123699
## A.npnct09.log 4.775988e-03 <NA> 170.842105 0.06123699
## A.npnct27.log 5.547032e-03 <NA> 6531.000000 0.03061849
## A.npnct11.log 5.547032e-03 <NA> 6531.000000 0.03061849
## H.npnct11.log 5.547032e-03 <NA> 6531.000000 0.03061849
## H.npnct22.log 5.547032e-03 <NA> 6531.000000 0.03061849
## S.npnct02.log 5.547032e-03 <NA> 6531.000000 0.03061849
## S.npnct11.log 5.547032e-03 <NA> 6531.000000 0.03061849
## PubDate.last100.log 7.663322e-03 <NA> 25.000000 92.19228414
## H.npnct05.log 9.653967e-03 <NA> 543.333333 0.03061849
## PubDate.date.fctr 1.164756e-02 <NA> 1.021394 0.07654623
## PubDate.second.fctr 1.187946e-02 <NA> 1.018204 0.06123699
## H.npnct07.log 1.201741e-02 <NA> 5.437234 0.12247397
## A.npnct07.log 1.214357e-02 <NA> 1631.750000 0.04592774
## S.npnct07.log 1.214357e-02 <NA> 1631.750000 0.04592774
## S.npnct03.log 1.240734e-02 <NA> 1305.400000 0.03061849
## A.npnct19.log 1.271661e-02 <NA> 1631.500000 0.06123699
## H.npnct13.log 1.305305e-02 <NA> 13.126638 0.09185548
## A.has.http 1.359260e-02 <NA> 1087.666667 0.03061849
## A.npnct03.log 1.359260e-02 <NA> 1087.666667 0.03061849
## A.npnct02.log 1.451467e-02 <NA> 1087.500000 0.04592774
## A.npnct18.log 1.451467e-02 <NA> 1087.500000 0.04592774
## A.npnct20.log 1.451467e-02 <NA> 1087.500000 0.04592774
## A.has.year.colon 1.755336e-02 <NA> 652.200000 0.03061849
## S.has.year.colon 1.755336e-02 <NA> 652.200000 0.03061849
## A.npnct22.log 1.923169e-02 S.npnct22.log 543.333333 0.03061849
## S.npnct22.log 1.923169e-02 <NA> 543.333333 0.03061849
## H.npnct02.log 2.001851e-02 <NA> 501.461538 0.03061849
## A.presid.log 2.014404e-02 S.presid.log 26.854701 0.06123699
## S.presid.log 2.014404e-02 <NA> 26.854701 0.06123699
## S.npnct15.log 2.121844e-02 <NA> 203.062500 0.04592774
## A.npnct06.log 2.389145e-02 S.npnct06.log 115.642857 0.03061849
## S.npnct06.log 2.389145e-02 <NA> 115.642857 0.03061849
## A.npnct15.log 2.407715e-02 <NA> 196.696970 0.10716473
## H.npnct14.log 2.524770e-02 <NA> 22.802326 0.12247397
## S.take.log 2.569295e-02 <NA> 29.376744 0.04592774
## A.take.log 2.601772e-02 S.take.log 29.236111 0.04592774
## PubDate.minute.fctr 3.407385e-02 <NA> 1.483365 0.06123699
## S.new.log 3.483189e-02 <NA> 10.124573 0.04592774
## A.new.log 3.524871e-02 S.new.log 10.086735 0.04592774
## S.npnct13.log 3.638891e-02 <NA> 5.706263 0.09185548
## A.npnct13.log 3.760012e-02 S.npnct13.log 5.715368 0.12247397
## PubDate.wkday.fctr 3.980129e-02 <NA> 1.003268 0.10716473
## S.npnct30.log 4.370037e-02 <NA> 134.791667 0.04592774
## A.npnct30.log 4.373349e-02 S.npnct30.log 126.862745 0.04592774
## S.day.log 4.555421e-02 <NA> 24.692913 0.04592774
## A.day.log 4.581783e-02 S.day.log 24.592157 0.04592774
## H.X2014.log 4.620638e-02 <NA> 63.673267 0.03061849
## A.show.log 4.897915e-02 S.show.log 30.512077 0.06123699
## S.show.log 4.897915e-02 <NA> 30.512077 0.06123699
## A.npnct14.log 4.999563e-02 <NA> 4.603330 0.16840171
## A.report.log 5.032801e-02 S.report.log 24.204633 0.06123699
## S.report.log 5.032801e-02 <NA> 24.204633 0.06123699
## A.year.log 5.094457e-02 S.year.log 18.456716 0.06123699
## S.year.log 5.094457e-02 <NA> 18.456716 0.06123699
## H.npnct04.log 5.126277e-02 <NA> 38.325301 0.04592774
## A.share.log 5.138139e-02 S.share.log 32.654639 0.04592774
## S.share.log 5.138139e-02 <NA> 32.654639 0.04592774
## S.compani.log 5.261812e-02 <NA> 18.093842 0.04592774
## A.compani.log 5.268413e-02 S.compani.log 18.147059 0.04592774
## H.new.log 5.313316e-02 <NA> 25.228916 0.04592774
## S.npnct14.log 5.332519e-02 A.npnct14.log 4.672000 0.16840171
## A.first.log 5.345938e-02 S.first.log 29.509346 0.04592774
## S.first.log 5.345938e-02 <NA> 29.509346 0.04592774
## S.time.log 5.759227e-02 <NA> 13.483296 0.04592774
## A.time.log 5.779371e-02 S.time.log 13.451111 0.04592774
## H.newyork.log 5.797009e-02 <NA> 26.795745 0.03061849
## A.articl.log 5.952055e-02 S.articl.log 30.863415 0.03061849
## S.articl.log 5.952055e-02 <NA> 30.863415 0.03061849
## S.will.log 6.103349e-02 <NA> 11.237288 0.06123699
## A.will.log 6.147068e-02 S.will.log 11.212406 0.06123699
## H.npnct15.log 6.158577e-02 <NA> 52.983471 0.03061849
## A.newyork.log 6.219997e-02 S.newyork.log 15.153465 0.06123699
## S.newyork.log 6.219997e-02 <NA> 15.153465 0.06123699
## H.day.log 6.272898e-02 <NA> 29.801887 0.04592774
## A.npnct04.log 6.294642e-02 S.npnct04.log 28.536364 0.07654623
## S.npnct04.log 6.294642e-02 <NA> 28.536364 0.07654623
## H.today.log 6.372306e-02 <NA> 36.757225 0.03061849
## H.report.log 6.494810e-02 <NA> 30.403846 0.03061849
## H.X2015.log 6.658489e-02 H.npnct15.log 45.326241 0.03061849
## S.npnct16.log 6.770952e-02 <NA> 13.647191 0.04592774
## A.intern.log 6.864274e-02 S.intern.log 29.801887 0.04592774
## S.intern.log 6.864274e-02 <NA> 29.801887 0.04592774
## A.npnct16.log 6.893301e-02 S.npnct16.log 13.482222 0.04592774
## H.daili.log 6.919298e-02 <NA> 41.973684 0.03061849
## H.week.log 7.510522e-02 <NA> 24.818182 0.03061849
## H.has.year.colon 7.842875e-02 A.intern.log 32.670103 0.03061849
## H.fashion.log 8.204998e-02 H.week.log 28.542986 0.04592774
## H.npnct16.log 8.273237e-02 <NA> 3.914910 0.04592774
## A.fashion.log 8.724932e-02 S.fashion.log 25.737705 0.04592774
## S.fashion.log 8.724932e-02 <NA> 25.737705 0.04592774
## A.week.log 8.840293e-02 S.week.log 13.278509 0.04592774
## S.week.log 8.840293e-02 <NA> 13.278509 0.04592774
## H.npnct30.log 8.917338e-02 <NA> 24.123077 0.03061849
## S.npnct12.log 9.158156e-02 <NA> 1.660473 0.13778322
## A.npnct12.log 9.183870e-02 S.npnct12.log 1.660473 0.13778322
## H.ndgts.log 1.196633e-01 <NA> 13.616137 0.18371096
## S.ndgts.log 1.242046e-01 <NA> 10.511247 0.26025720
## A.ndgts.log 1.249484e-01 S.ndgts.log 10.501022 0.29087569
## H.nuppr.log 1.278085e-01 <NA> 1.033930 0.29087569
## H.nchrs.log 1.710624e-01 <NA> 1.023810 1.57685242
## H.nwrds.log 2.006864e-01 <NA> 1.019119 0.21432945
## H.nwrds.unq.log 2.044964e-01 H.nuppr.log 1.019071 0.21432945
## A.nchrs.log 2.245488e-01 <NA> 1.328571 4.39375383
## S.nchrs.log 2.246930e-01 A.nchrs.log 1.328571 3.72014697
## A.nwrds.log 2.450733e-01 <NA> 1.029183 0.59706062
## S.nwrds.log 2.453541e-01 A.nwrds.log 1.029183 0.45927740
## A.nwrds.unq.log 2.506012e-01 <NA> 1.061567 0.55113288
## S.nwrds.unq.log 2.507969e-01 S.nchrs.log 1.061567 0.44396816
## S.nuppr.log 2.718459e-01 <NA> 1.152620 0.33680343
## A.nuppr.log 2.720962e-01 S.nuppr.log 1.151308 0.33680343
## A.npnct05.log NA <NA> 0.000000 0.01530925
## A.npnct10.log NA <NA> 0.000000 0.01530925
## A.npnct24.log NA <NA> 0.000000 0.01530925
## A.npnct28.log NA <NA> 0.000000 0.01530925
## A.npnct29.log NA <NA> 0.000000 0.01530925
## A.npnct31.log NA <NA> 0.000000 0.01530925
## A.npnct32.log NA <NA> 0.000000 0.01530925
## H.has.http NA <NA> 0.000000 0.01530925
## H.npnct10.log NA <NA> 0.000000 0.01530925
## H.npnct18.log NA <NA> 0.000000 0.01530925
## H.npnct19.log NA <NA> 0.000000 0.01530925
## H.npnct20.log NA <NA> 0.000000 0.01530925
## H.npnct23.log NA <NA> 0.000000 0.01530925
## H.npnct24.log NA <NA> 0.000000 0.01530925
## H.npnct25.log NA <NA> 0.000000 0.01530925
## H.npnct27.log NA <NA> 0.000000 0.01530925
## H.npnct28.log NA <NA> 0.000000 0.01530925
## H.npnct29.log NA <NA> 0.000000 0.01530925
## H.npnct31.log NA <NA> 0.000000 0.01530925
## H.npnct32.log NA <NA> 0.000000 0.01530925
## PubDate.year.fctr NA <NA> 0.000000 0.01530925
## S.has.http NA <NA> 0.000000 0.01530925
## S.npnct05.log NA <NA> 0.000000 0.01530925
## S.npnct10.log NA <NA> 0.000000 0.01530925
## S.npnct18.log NA <NA> 0.000000 0.01530925
## S.npnct19.log NA <NA> 0.000000 0.01530925
## S.npnct20.log NA <NA> 0.000000 0.01530925
## S.npnct24.log NA <NA> 0.000000 0.01530925
## S.npnct27.log NA <NA> 0.000000 0.01530925
## S.npnct28.log NA <NA> 0.000000 0.01530925
## S.npnct29.log NA <NA> 0.000000 0.01530925
## S.npnct31.log NA <NA> 0.000000 0.01530925
## S.npnct32.log NA <NA> 0.000000 0.01530925
## zeroVar nzv is.cor.y.abs.low
## Popular FALSE FALSE FALSE
## WordCount.log FALSE FALSE FALSE
## WordCount FALSE FALSE FALSE
## PubDate.hour.fctr FALSE FALSE FALSE
## H.npnct21.log FALSE FALSE FALSE
## PubDate.wkend FALSE FALSE FALSE
## S.npnct21.log FALSE FALSE FALSE
## A.npnct21.log FALSE FALSE FALSE
## PubDate.last10 FALSE FALSE FALSE
## H.npnct08.log FALSE TRUE FALSE
## H.npnct09.log FALSE TRUE FALSE
## PubDate.last10.log FALSE FALSE FALSE
## PubDate.last1.log FALSE FALSE FALSE
## PubDate.last100 FALSE FALSE FALSE
## PubDate.last1 FALSE FALSE FALSE
## H.npnct06.log FALSE TRUE FALSE
## A.can.log FALSE TRUE FALSE
## A.npnct01.log FALSE TRUE FALSE
## S.npnct01.log FALSE TRUE FALSE
## S.can.log FALSE TRUE FALSE
## H.npnct17.log FALSE TRUE FALSE
## S.npnct23.log FALSE TRUE FALSE
## S.npnct25.log FALSE TRUE FALSE
## H.has.ebola FALSE TRUE FALSE
## A.make.log FALSE TRUE FALSE
## S.make.log FALSE TRUE FALSE
## H.npnct01.log FALSE TRUE FALSE
## PubDate.month.fctr FALSE FALSE FALSE
## .rnorm FALSE FALSE FALSE
## PubDate.POSIX FALSE FALSE TRUE
## PubDate.zoo FALSE FALSE TRUE
## A.npnct23.log FALSE TRUE TRUE
## A.npnct25.log FALSE TRUE TRUE
## H.npnct12.log FALSE FALSE TRUE
## myCategory.fctr FALSE FALSE TRUE
## UniqueID FALSE FALSE TRUE
## H.npnct03.log FALSE TRUE TRUE
## S.state.log FALSE TRUE TRUE
## A.state.log FALSE TRUE TRUE
## S.one.log FALSE TRUE TRUE
## A.one.log FALSE TRUE TRUE
## A.said.log FALSE TRUE TRUE
## S.said.log FALSE TRUE TRUE
## A.npnct26.log TRUE TRUE TRUE
## H.npnct26.log TRUE TRUE TRUE
## S.npnct26.log TRUE TRUE TRUE
## A.npnct17.log FALSE TRUE TRUE
## S.npnct17.log FALSE TRUE TRUE
## S.npnct08.log FALSE TRUE TRUE
## A.npnct08.log FALSE TRUE TRUE
## S.npnct09.log FALSE TRUE TRUE
## A.npnct09.log FALSE TRUE TRUE
## A.npnct27.log FALSE TRUE TRUE
## A.npnct11.log FALSE TRUE TRUE
## H.npnct11.log FALSE TRUE TRUE
## H.npnct22.log FALSE TRUE TRUE
## S.npnct02.log FALSE TRUE TRUE
## S.npnct11.log FALSE TRUE TRUE
## PubDate.last100.log FALSE FALSE TRUE
## H.npnct05.log FALSE TRUE TRUE
## PubDate.date.fctr FALSE FALSE TRUE
## PubDate.second.fctr FALSE FALSE TRUE
## H.npnct07.log FALSE FALSE TRUE
## A.npnct07.log FALSE TRUE TRUE
## S.npnct07.log FALSE TRUE TRUE
## S.npnct03.log FALSE TRUE TRUE
## A.npnct19.log FALSE TRUE TRUE
## H.npnct13.log FALSE FALSE TRUE
## A.has.http FALSE TRUE TRUE
## A.npnct03.log FALSE TRUE TRUE
## A.npnct02.log FALSE TRUE TRUE
## A.npnct18.log FALSE TRUE TRUE
## A.npnct20.log FALSE TRUE TRUE
## A.has.year.colon FALSE TRUE TRUE
## S.has.year.colon FALSE TRUE TRUE
## A.npnct22.log FALSE TRUE FALSE
## S.npnct22.log FALSE TRUE FALSE
## H.npnct02.log FALSE TRUE FALSE
## A.presid.log FALSE TRUE FALSE
## S.presid.log FALSE TRUE FALSE
## S.npnct15.log FALSE TRUE FALSE
## A.npnct06.log FALSE TRUE FALSE
## S.npnct06.log FALSE TRUE FALSE
## A.npnct15.log FALSE TRUE FALSE
## H.npnct14.log FALSE TRUE FALSE
## S.take.log FALSE TRUE FALSE
## A.take.log FALSE TRUE FALSE
## PubDate.minute.fctr FALSE FALSE FALSE
## S.new.log FALSE FALSE FALSE
## A.new.log FALSE FALSE FALSE
## S.npnct13.log FALSE FALSE FALSE
## A.npnct13.log FALSE FALSE FALSE
## PubDate.wkday.fctr FALSE FALSE FALSE
## S.npnct30.log FALSE TRUE FALSE
## A.npnct30.log FALSE TRUE FALSE
## S.day.log FALSE TRUE FALSE
## A.day.log FALSE TRUE FALSE
## H.X2014.log FALSE TRUE FALSE
## A.show.log FALSE TRUE FALSE
## S.show.log FALSE TRUE FALSE
## A.npnct14.log FALSE FALSE FALSE
## A.report.log FALSE TRUE FALSE
## S.report.log FALSE TRUE FALSE
## A.year.log FALSE FALSE FALSE
## S.year.log FALSE FALSE FALSE
## H.npnct04.log FALSE TRUE FALSE
## A.share.log FALSE TRUE FALSE
## S.share.log FALSE TRUE FALSE
## S.compani.log FALSE FALSE FALSE
## A.compani.log FALSE FALSE FALSE
## H.new.log FALSE TRUE FALSE
## S.npnct14.log FALSE FALSE FALSE
## A.first.log FALSE TRUE FALSE
## S.first.log FALSE TRUE FALSE
## S.time.log FALSE FALSE FALSE
## A.time.log FALSE FALSE FALSE
## H.newyork.log FALSE TRUE FALSE
## A.articl.log FALSE TRUE FALSE
## S.articl.log FALSE TRUE FALSE
## S.will.log FALSE FALSE FALSE
## A.will.log FALSE FALSE FALSE
## H.npnct15.log FALSE TRUE FALSE
## A.newyork.log FALSE FALSE FALSE
## S.newyork.log FALSE FALSE FALSE
## H.day.log FALSE TRUE FALSE
## A.npnct04.log FALSE TRUE FALSE
## S.npnct04.log FALSE TRUE FALSE
## H.today.log FALSE TRUE FALSE
## H.report.log FALSE TRUE FALSE
## H.X2015.log FALSE TRUE FALSE
## S.npnct16.log FALSE FALSE FALSE
## A.intern.log FALSE TRUE FALSE
## S.intern.log FALSE TRUE FALSE
## A.npnct16.log FALSE FALSE FALSE
## H.daili.log FALSE TRUE FALSE
## H.week.log FALSE TRUE FALSE
## H.has.year.colon FALSE TRUE FALSE
## H.fashion.log FALSE TRUE FALSE
## H.npnct16.log FALSE FALSE FALSE
## A.fashion.log FALSE TRUE FALSE
## S.fashion.log FALSE TRUE FALSE
## A.week.log FALSE FALSE FALSE
## S.week.log FALSE FALSE FALSE
## H.npnct30.log FALSE TRUE FALSE
## S.npnct12.log FALSE FALSE FALSE
## A.npnct12.log FALSE FALSE FALSE
## H.ndgts.log FALSE FALSE FALSE
## S.ndgts.log FALSE FALSE FALSE
## A.ndgts.log FALSE FALSE FALSE
## H.nuppr.log FALSE FALSE FALSE
## H.nchrs.log FALSE FALSE FALSE
## H.nwrds.log FALSE FALSE FALSE
## H.nwrds.unq.log FALSE FALSE FALSE
## A.nchrs.log FALSE FALSE FALSE
## S.nchrs.log FALSE FALSE FALSE
## A.nwrds.log FALSE FALSE FALSE
## S.nwrds.log FALSE FALSE FALSE
## A.nwrds.unq.log FALSE FALSE FALSE
## S.nwrds.unq.log FALSE FALSE FALSE
## S.nuppr.log FALSE FALSE FALSE
## A.nuppr.log FALSE FALSE FALSE
## A.npnct05.log TRUE TRUE NA
## A.npnct10.log TRUE TRUE NA
## A.npnct24.log TRUE TRUE NA
## A.npnct28.log TRUE TRUE NA
## A.npnct29.log TRUE TRUE NA
## A.npnct31.log TRUE TRUE NA
## A.npnct32.log TRUE TRUE NA
## H.has.http TRUE TRUE NA
## H.npnct10.log TRUE TRUE NA
## H.npnct18.log TRUE TRUE NA
## H.npnct19.log TRUE TRUE NA
## H.npnct20.log TRUE TRUE NA
## H.npnct23.log TRUE TRUE NA
## H.npnct24.log TRUE TRUE NA
## H.npnct25.log TRUE TRUE NA
## H.npnct27.log TRUE TRUE NA
## H.npnct28.log TRUE TRUE NA
## H.npnct29.log TRUE TRUE NA
## H.npnct31.log TRUE TRUE NA
## H.npnct32.log TRUE TRUE NA
## PubDate.year.fctr TRUE TRUE NA
## S.has.http TRUE TRUE NA
## S.npnct05.log TRUE TRUE NA
## S.npnct10.log TRUE TRUE NA
## S.npnct18.log TRUE TRUE NA
## S.npnct19.log TRUE TRUE NA
## S.npnct20.log TRUE TRUE NA
## S.npnct24.log TRUE TRUE NA
## S.npnct27.log TRUE TRUE NA
## S.npnct28.log TRUE TRUE NA
## S.npnct29.log TRUE TRUE NA
## S.npnct31.log TRUE TRUE NA
## S.npnct32.log TRUE TRUE NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 select.features 4 0 148.235 175.347 27.112
## 8 partition.data.training 5 0 175.348 NA NAStep 5.0: partition data training
if (all(is.na(glb_newent_df[, glb_rsp_var]))) {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnent_df[, glb_rsp_var_raw],
SplitRatio=1 - (nrow(glb_newent_df) * 1.1 / nrow(glb_trnent_df)))
glb_fitent_df <- glb_trnent_df[split, ]
glb_OOBent_df <- glb_trnent_df[!split ,]
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitent_df <- glb_trnent_df; glb_OOBent_df <- glb_newent_df
}## Loading required package: caToolsif (!is.null(glb_max_fitent_obs) && (nrow(glb_fitent_df) > glb_max_fitent_obs)) {
warning("glb_fitent_df restricted to glb_max_fitent_obs: ",
format(glb_max_fitent_obs, big.mark=","))
org_fitent_df <- glb_fitent_df
glb_fitent_df <-
org_fitent_df[split <- sample.split(org_fitent_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitent_obs), ]
org_fitent_df <- NULL
}
sav_entity_df <- glb_entity_df
glb_entity_df$.lcn <- ""
glb_entity_df[glb_entity_df[, glb_id_vars] %in%
glb_fitent_df[, glb_id_vars], ".lcn"] <- "Fit"
glb_entity_df[glb_entity_df[, glb_id_vars] %in%
glb_OOBent_df[, glb_id_vars], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
dsp_class_dstrb(glb_entity_df, ".lcn", glb_rsp_var_raw)## Popular.0 Popular.1 Popular.NA
## NA NA 1870
## Fit 3726 749 NA
## OOB 1713 344 NA
## Popular.0 Popular.1 Popular.NA
## NA NA 1
## Fit 0.8326257 0.1673743 NA
## OOB 0.8327662 0.1672338 NAnewent_ctgry_df <- mycreate_sqlxtab_df(subset(glb_entity_df, .src == "Test"),
"myCategory")
OOBent_ctgry_df <- mycreate_sqlxtab_df(subset(glb_entity_df, .lcn == "OOB"),
"myCategory")
glb_ctgry_df <- merge(newent_ctgry_df, OOBent_ctgry_df, by="myCategory", all=TRUE,
suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))## myCategory .n.Tst .n.OOB .freqRatio.Tst
## 1 ## 338 407 0.180748663
## 6 Business#Business Day#Dealbook 304 312 0.162566845
## 10 Culture#Arts# 244 225 0.130481283
## 15 OpEd#Opinion# 164 154 0.087700535
## 9 Business#Technology# 113 114 0.060427807
## 20 TStyle## 105 221 0.056149733
## 5 #U.S.#Education 90 93 0.048128342
## 13 Metro#N.Y. / Region# 67 60 0.035828877
## 18 Styles#U.S.# 62 54 0.033155080
## 16 Science#Health# 57 66 0.030481283
## 12 Foreign#World#Asia Pacific 56 61 0.029946524
## 2 #Multimedia# 52 42 0.027807487
## 11 Foreign#World# 47 47 0.025133690
## 7 Business#Business Day#Small Business 42 45 0.022459893
## 8 Business#Crosswords/Games# 42 40 0.022459893
## 19 Travel#Travel# 35 31 0.018716578
## 3 #Opinion#Room For Debate 24 21 0.012834225
## 17 Styles##Fashion 15 41 0.008021390
## 4 #Opinion#The Public Editor 10 10 0.005347594
## 14 myOther 3 13 0.001604278
## .freqRatio.OOB
## 1 0.197860963
## 6 0.151677200
## 10 0.109382596
## 15 0.074866310
## 9 0.055420515
## 20 0.107438017
## 5 0.045211473
## 13 0.029168692
## 18 0.026251823
## 16 0.032085561
## 12 0.029654837
## 2 0.020418085
## 11 0.022848809
## 7 0.021876519
## 8 0.019445795
## 19 0.015070491
## 3 0.010209042
## 17 0.019931940
## 4 0.004861449
## 14 0.006319883# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 194 10sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_vars) && glb_id_vars != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_vars, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## Popular.fctr Popular.fctr TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## Popular Popular 1.00000000 TRUE 1.00000000 <NA>
## UniqueID UniqueID 0.01182492 TRUE 0.01182492 <NA>
## Popular.fctr Popular.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## Popular 4.976212 0.03061849 FALSE FALSE FALSE
## UniqueID 1.000000 100.00000000 FALSE FALSE TRUE
## Popular.fctr NA NA NA NA NA
## rsp_var_raw id_var rsp_var
## Popular TRUE NA NA
## UniqueID FALSE TRUE NA
## Popular.fctr NA NA TRUEprint("glb_feats_df vs. glb_entity_df: "); ## [1] "glb_feats_df vs. glb_entity_df: "print(setdiff(glb_feats_df$id, names(glb_entity_df)))## character(0)print("glb_entity_df vs. glb_feats_df: "); ## [1] "glb_entity_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_entity_df), glb_feats_df$id),
myfind_chr_cols_df(glb_entity_df)))## character(0)#print(setdiff(setdiff(names(glb_entity_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_entity_df: "); print(dim(glb_entity_df))## [1] "glb_entity_df: "## [1] 8402 205print("glb_trnent_df: "); print(dim(glb_trnent_df))## [1] "glb_trnent_df: "## [1] 6532 204print("glb_fitent_df: "); print(dim(glb_fitent_df))## [1] "glb_fitent_df: "## [1] 4475 204print("glb_OOBent_df: "); print(dim(glb_OOBent_df))## [1] "glb_OOBent_df: "## [1] 2057 204print("glb_newent_df: "); print(dim(glb_newent_df))## [1] "glb_newent_df: "## [1] 1870 204# sav_entity_df <- glb_entity_df
# glb_entity_df <- sav_entity_df
# # Does not handle NULL or length(glb_id_vars) > 1
# glb_entity_df$.src.trn <- 0
# glb_entity_df[glb_entity_df[, glb_id_vars] %in% glb_trnent_df[, glb_id_vars],
# ".src.trn"] <- 1
# glb_entity_df$.src.fit <- 0
# glb_entity_df[glb_entity_df[, glb_id_vars] %in% glb_fitent_df[, glb_id_vars],
# ".src.fit"] <- 1
# glb_entity_df$.src.OOB <- 0
# glb_entity_df[glb_entity_df[, glb_id_vars] %in% glb_OOBent_df[, glb_id_vars],
# ".src.OOB"] <- 1
# glb_entity_df$.src.new <- 0
# glb_entity_df[glb_entity_df[, glb_id_vars] %in% glb_newent_df[, glb_id_vars],
# ".src.new"] <- 1
# #print(unique(glb_entity_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_entity_df <- glb_entity_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_entity_df
if (glb_save_envir)
save(glb_feats_df,
glb_entity_df, #glb_trnent_df, glb_fitent_df, glb_OOBent_df, glb_newent_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_entity_df))
# stop("glb_entity_df r/w not working")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 partition.data.training 5 0 175.348 176.476 1.128
## 9 fit.models 6 0 176.477 NA NAStep 6.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_entity_df),
# grep("^.src", names(glb_entity_df), value=TRUE))
# glb_trnent_df <- glb_entity_df[glb_entity_df$.src.trn == 1, keep_cols]
# glb_fitent_df <- glb_entity_df[glb_entity_df$.src.fit == 1, keep_cols]
# glb_OOBent_df <- glb_entity_df[glb_entity_df$.src.OOB == 1, keep_cols]
# glb_newent_df <- glb_entity_df[glb_entity_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitent_df[, glb_rsp_var])) < 2))
stop("glb_fitent_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitent_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_var <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[1, "id"]
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_var != glb_Baseline_mdl_var) &
(glb_feats_df[max_cor_y_x_var, "cor.y.abs"] >
glb_feats_df[glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_var, " has a lower correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl_fn(model_id="Baseline", model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitent_df, OOB_df=glb_OOBent_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitent_df, OOB_df=glb_OOBent_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.8326257 0.1673743
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8326257 0.1673743
## 2 0.8326257 0.1673743
## 3 0.8326257 0.1673743
## 4 0.8326257 0.1673743
## 5 0.8326257 0.1673743
## 6 0.8326257 0.1673743
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.MFO.myMFO_classfr.N
## 1 N 3726
## 2 Y 749
## Prediction
## Reference N Y
## N 3726 0
## Y 749 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.326257e-01 0.000000e+00 8.213602e-01 8.434553e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 5.097571e-01 1.800616e-164
## [1] " calling mypredict_mdl for OOB:"
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8326257 0.1673743
## 2 0.8326257 0.1673743
## 3 0.8326257 0.1673743
## 4 0.8326257 0.1673743
## 5 0.8326257 0.1673743
## 6 0.8326257 0.1673743
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.MFO.myMFO_classfr.N
## 1 N 1713
## 2 Y 344
## Prediction
## Reference N Y
## N 1713 0
## Y 344 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.327662e-01 0.000000e+00 8.159247e-01 8.486533e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 5.143944e-01 2.337097e-76
## model_id model_method feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.673 0.003 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8326257
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8213602 0.8434553 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8327662
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8159247 0.8486533 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitent_df, OOB_df=glb_OOBent_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.2867534
## 3 0.2 0.1735751
## 4 0.3 0.1735751
## 5 0.4 0.1735751
## 6 0.5 0.1735751
## 7 0.6 0.1735751
## 8 0.7 0.1735751
## 9 0.8 0.1735751
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Random.myrandom_classfr.Y
## 1 N 3726
## 2 Y 749
## Prediction
## Reference N Y
## N 0 3726
## Y 0 749
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.1673743 0.0000000 0.1565447 0.1786398 0.8326257
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] " calling mypredict_mdl for OOB:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.2865473
## 3 0.2 0.1547278
## 4 0.3 0.1547278
## 5 0.4 0.1547278
## 6 0.5 0.1547278
## 7 0.6 0.1547278
## 8 0.7 0.1547278
## 9 0.8 0.1547278
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Random.myrandom_classfr.Y
## 1 N 1713
## 2 Y 344
## Prediction
## Reference N Y
## N 0 1713
## Y 0 344
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.1672338 0.0000000 0.1513467 0.1840753 0.8327662
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## model_id model_method feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.338 0.001 0.5007516
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.1 0.2867534 0.1673743
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.1565447 0.1786398 0 0.4909227
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.2865473 0.1672338
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.1513467 0.1840753 0# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitent_df, OOB_df=glb_OOBent_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: A.nuppr.log"## Loading required package: rpart## Fitting cp = 0 on full training set## Loading required package: rpart.plot## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4475
##
## CP nsplit rel error
## 1 0 0 1
##
## Node number 1: 4475 observations
## predicted class=N expected loss=0.1673743 P(node) =1
## class counts: 3726 749
## probabilities: 0.833 0.167
##
## n= 4475
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4475 749 N (0.8326257 0.1673743) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 3726
## 2 Y 749
## Prediction
## Reference N Y
## N 3726 0
## Y 749 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.326257e-01 0.000000e+00 8.213602e-01 8.434553e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 5.097571e-01 1.800616e-164
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 1713
## 2 Y 344
## Prediction
## Reference N Y
## N 1713 0
## Y 344 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.327662e-01 0.000000e+00 8.159247e-01 8.486533e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 5.143944e-01 2.337097e-76
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.rpart rpart A.nuppr.log 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.659 0.054 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8326257
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8213602 0.8434553 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8327662
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8159247 0.8486533 0ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitent_df, OOB_df=glb_OOBent_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: A.nuppr.log"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4475
##
## CP nsplit rel error
## 1 0 0 1
##
## Node number 1: 4475 observations
## predicted class=N expected loss=0.1673743 P(node) =1
## class counts: 3726 749
## probabilities: 0.833 0.167
##
## n= 4475
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4475 749 N (0.8326257 0.1673743) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 3726
## 2 Y 749
## Prediction
## Reference N Y
## N 3726 0
## Y 749 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.326257e-01 0.000000e+00 8.213602e-01 8.434553e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 5.097571e-01 1.800616e-164
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 1713
## 2 Y 344
## Prediction
## Reference N Y
## N 1713 0
## Y 344 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.327662e-01 0.000000e+00 8.159247e-01 8.486533e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 5.143944e-01 2.337097e-76
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart A.nuppr.log 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.595 0.053 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8326257
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8213602 0.8434553 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8327662
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8159247 0.8486533 0if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitent_df, OOB_df=glb_OOBent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: A.nuppr.log"
## + Fold1: cp=0
## - Fold1: cp=0
## + Fold2: cp=0
## - Fold2: cp=0
## + Fold3: cp=0
## - Fold3: cp=0
## Aggregating results
## Fitting final model on full training set## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4475
##
## CP nsplit rel error
## 1 0 0 1
##
## Node number 1: 4475 observations
## predicted class=N expected loss=0.1673743 P(node) =1
## class counts: 3726 749
## probabilities: 0.833 0.167
##
## n= 4475
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4475 749 N (0.8326257 0.1673743) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.rpart.N
## 1 N 3726
## 2 Y 749
## Prediction
## Reference N Y
## N 3726 0
## Y 749 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.326257e-01 0.000000e+00 8.213602e-01 8.434553e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 5.097571e-01 1.800616e-164
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.rpart.N
## 1 N 1713
## 2 Y 344
## Prediction
## Reference N Y
## N 1713 0
## Y 344 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.327662e-01 0.000000e+00 8.159247e-01 8.486533e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 5.143944e-01 2.337097e-76
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart A.nuppr.log 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.196 0.053 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8326258
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8213602 0.8434553 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8327662
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8159247 0.8486533 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0002791548 0# Used to compare vs. Interactions.High.cor.Y
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitent_df, OOB_df=glb_OOBent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.glm"
## [1] " indep_vars: A.nuppr.log"
## + Fold1: parameter=none
## - Fold1: parameter=none
## + Fold2: parameter=none
## - Fold2: parameter=none
## + Fold3: parameter=none
## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3585 -0.6318 -0.4867 -0.3464 2.6336
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.41620 0.11470 3.628 0.000285 ***
## A.nuppr.log -1.38947 0.08027 -17.310 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 3710.6 on 4473 degrees of freedom
## AIC: 3714.6
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.3499729
## 3 0.2 0.3986014
## 4 0.3 0.3121547
## 5 0.4 0.0000000
## 6 0.5 0.0000000
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.glm.N
## 1 N 2872
## 2 Y 350
## Popular.fctr.predict.Max.cor.Y.glm.Y
## 1 854
## 2 399
## Prediction
## Reference N Y
## N 2872 854
## Y 350 399
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.309497e-01 2.392074e-01 7.176970e-01 7.439004e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.280095e-47
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.3485577
## 3 0.2 0.3880266
## 4 0.3 0.3465046
## 5 0.4 0.0000000
## 6 0.5 0.0000000
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.glm.N
## 1 N 1330
## 2 Y 169
## Popular.fctr.predict.Max.cor.Y.glm.Y
## 1 383
## 2 175
## Prediction
## Reference N Y
## N 1330 383
## Y 169 175
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.316480e-01 2.283681e-01 7.119353e-01 7.506985e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.236001e-19
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.glm glm A.nuppr.log 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.122 0.077 0.7073742
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.2 0.3986014 0.8324022
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.717697 0.7439004 -0.0004459345 0.710206
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.3880266 0.731648
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7119353 0.7506985 0.2283681 3714.601
## max.AccuracySD.fit max.KappaSD.fit
## 1 6.48833e-05 0.0007723812# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_var, paste(max_cor_y_x_var, int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_var, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitent_df, OOB_df=glb_OOBent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.glm"
## [1] " indep_vars: A.nuppr.log, A.nuppr.log:A.npnct21.log, A.nuppr.log:H.npnct09.log, A.nuppr.log:H.npnct17.log, A.nuppr.log:S.can.log, A.nuppr.log:S.npnct01.log, A.nuppr.log:S.npnct25.log, A.nuppr.log:S.make.log, A.nuppr.log:S.npnct22.log, A.nuppr.log:S.presid.log, A.nuppr.log:S.npnct06.log, A.nuppr.log:S.take.log, A.nuppr.log:S.new.log, A.nuppr.log:S.npnct13.log, A.nuppr.log:S.npnct30.log, A.nuppr.log:S.day.log, A.nuppr.log:S.show.log, A.nuppr.log:S.report.log, A.nuppr.log:S.year.log, A.nuppr.log:S.share.log, A.nuppr.log:S.compani.log, A.nuppr.log:A.npnct14.log, A.nuppr.log:S.first.log, A.nuppr.log:S.time.log, A.nuppr.log:S.articl.log, A.nuppr.log:S.will.log, A.nuppr.log:S.newyork.log, A.nuppr.log:S.npnct04.log, A.nuppr.log:H.npnct15.log, A.nuppr.log:S.intern.log, A.nuppr.log:S.npnct16.log, A.nuppr.log:A.intern.log, A.nuppr.log:H.week.log, A.nuppr.log:S.fashion.log, A.nuppr.log:S.week.log, A.nuppr.log:S.npnct12.log, A.nuppr.log:S.ndgts.log, A.nuppr.log:H.nuppr.log, A.nuppr.log:A.nchrs.log, A.nuppr.log:A.nwrds.log, A.nuppr.log:S.nchrs.log, A.nuppr.log:S.nuppr.log"
## + Fold1: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1: parameter=none
## + Fold2: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2: parameter=none
## + Fold3: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6831 -0.6735 -0.3803 -0.1007 3.1747
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.51755 0.31303 -1.653 0.098259 .
## A.nuppr.log 1.58694 0.73421 2.161 0.030662 *
## `A.nuppr.log:A.npnct21.log` 0.48451 0.14058 3.447 0.000568 ***
## `A.nuppr.log:H.npnct09.log` 0.59295 0.35062 1.691 0.090812 .
## `A.nuppr.log:H.npnct17.log` 0.75107 0.25976 2.891 0.003836 **
## `A.nuppr.log:S.can.log` 0.16811 0.24241 0.693 0.488010
## `A.nuppr.log:S.npnct01.log` 0.86097 0.46656 1.845 0.064986 .
## `A.nuppr.log:S.npnct25.log` NA NA NA NA
## `A.nuppr.log:S.make.log` 0.19167 0.21838 0.878 0.380128
## `A.nuppr.log:S.npnct22.log` -15.22448 2878.71441 -0.005 0.995780
## `A.nuppr.log:S.presid.log` -0.07309 0.20738 -0.352 0.724517
## `A.nuppr.log:S.npnct06.log` -0.70473 0.83971 -0.839 0.401328
## `A.nuppr.log:S.take.log` -0.39937 0.31344 -1.274 0.202598
## `A.nuppr.log:S.new.log` -0.19679 0.17118 -1.150 0.250301
## `A.nuppr.log:S.npnct13.log` -0.04293 0.10057 -0.427 0.669486
## `A.nuppr.log:S.npnct30.log` -8.49555 618.44969 -0.014 0.989040
## `A.nuppr.log:S.day.log` -0.51422 0.34671 -1.483 0.138034
## `A.nuppr.log:S.show.log` -0.72107 0.37336 -1.931 0.053447 .
## `A.nuppr.log:S.report.log` -0.83927 0.34820 -2.410 0.015939 *
## `A.nuppr.log:S.year.log` -0.14977 0.24361 -0.615 0.538699
## `A.nuppr.log:S.share.log` -0.88180 0.37947 -2.324 0.020137 *
## `A.nuppr.log:S.compani.log` -0.62160 0.25841 -2.406 0.016150 *
## `A.nuppr.log:A.npnct14.log` 0.73332 0.10767 6.811 9.72e-12 ***
## `A.nuppr.log:S.first.log` -0.37160 0.30362 -1.224 0.220997
## `A.nuppr.log:S.time.log` -0.21555 0.19891 -1.084 0.278522
## `A.nuppr.log:S.articl.log` -1.49200 0.48351 -3.086 0.002030 **
## `A.nuppr.log:S.will.log` -0.58456 0.19448 -3.006 0.002650 **
## `A.nuppr.log:S.newyork.log` 0.43449 0.19000 2.287 0.022208 *
## `A.nuppr.log:S.npnct04.log` -1.05434 0.43307 -2.435 0.014910 *
## `A.nuppr.log:H.npnct15.log` -27.28745 897.98766 -0.030 0.975758
## `A.nuppr.log:S.intern.log` -1.22096 0.56865 -2.147 0.031784 *
## `A.nuppr.log:S.npnct16.log` -0.30827 0.21665 -1.423 0.154767
## `A.nuppr.log:A.intern.log` NA NA NA NA
## `A.nuppr.log:H.week.log` -1.47194 0.66636 -2.209 0.027179 *
## `A.nuppr.log:S.fashion.log` -30.13742 707.57791 -0.043 0.966026
## `A.nuppr.log:S.week.log` -0.62998 0.27246 -2.312 0.020766 *
## `A.nuppr.log:S.npnct12.log` -0.02261 0.06733 -0.336 0.736995
## `A.nuppr.log:S.ndgts.log` -0.26268 0.07520 -3.493 0.000477 ***
## `A.nuppr.log:H.nuppr.log` -0.44239 0.09373 -4.720 2.36e-06 ***
## `A.nuppr.log:A.nchrs.log` -3.21628 3.16370 -1.017 0.309333
## `A.nuppr.log:A.nwrds.log` -0.57056 0.29004 -1.967 0.049161 *
## `A.nuppr.log:S.nchrs.log` 3.30924 3.16954 1.044 0.296451
## `A.nuppr.log:S.nuppr.log` -0.49206 0.18678 -2.634 0.008429 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 3337.3 on 4434 degrees of freedom
## AIC: 3419.3
##
## Number of Fisher Scoring iterations: 18
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.28675345
## 2 0.1 0.39734300
## 3 0.2 0.45756458
## 4 0.3 0.46658851
## 5 0.4 0.35462345
## 6 0.5 0.12546125
## 7 0.6 0.01055409
## 8 0.7 0.00000000
## 9 0.8 0.00000000
## 10 0.9 0.00000000
## 11 1.0 0.00000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 3167
## 2 Y 351
## Popular.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 559
## 2 398
## Prediction
## Reference N Y
## N 3167 559
## Y 351 398
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.966480e-01 3.432664e-01 7.845519e-01 8.083558e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 6.791106e-12
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.28654727
## 2 0.1 0.39043825
## 3 0.2 0.45036765
## 4 0.3 0.45956354
## 5 0.4 0.33884298
## 6 0.5 0.10840108
## 7 0.6 0.03418803
## 8 0.7 0.01156069
## 9 0.8 0.00000000
## 10 0.9 0.00000000
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 1457
## 2 Y 165
## Popular.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 256
## 2 179
## Prediction
## Reference N Y
## N 1457 256
## Y 165 179
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.953330e-01 3.354449e-01 7.772394e-01 8.125808e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 9.999959e-01 1.152783e-05
## model_id model_method
## 1 Interact.High.cor.Y.glm glm
## feats
## 1 A.nuppr.log, A.nuppr.log:A.npnct21.log, A.nuppr.log:H.npnct09.log, A.nuppr.log:H.npnct17.log, A.nuppr.log:S.can.log, A.nuppr.log:S.npnct01.log, A.nuppr.log:S.npnct25.log, A.nuppr.log:S.make.log, A.nuppr.log:S.npnct22.log, A.nuppr.log:S.presid.log, A.nuppr.log:S.npnct06.log, A.nuppr.log:S.take.log, A.nuppr.log:S.new.log, A.nuppr.log:S.npnct13.log, A.nuppr.log:S.npnct30.log, A.nuppr.log:S.day.log, A.nuppr.log:S.show.log, A.nuppr.log:S.report.log, A.nuppr.log:S.year.log, A.nuppr.log:S.share.log, A.nuppr.log:S.compani.log, A.nuppr.log:A.npnct14.log, A.nuppr.log:S.first.log, A.nuppr.log:S.time.log, A.nuppr.log:S.articl.log, A.nuppr.log:S.will.log, A.nuppr.log:S.newyork.log, A.nuppr.log:S.npnct04.log, A.nuppr.log:H.npnct15.log, A.nuppr.log:S.intern.log, A.nuppr.log:S.npnct16.log, A.nuppr.log:A.intern.log, A.nuppr.log:H.week.log, A.nuppr.log:S.fashion.log, A.nuppr.log:S.week.log, A.nuppr.log:S.npnct12.log, A.nuppr.log:S.ndgts.log, A.nuppr.log:H.nuppr.log, A.nuppr.log:A.nchrs.log, A.nuppr.log:A.nwrds.log, A.nuppr.log:S.nchrs.log, A.nuppr.log:S.nuppr.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 3.489 0.818
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.792282 0.3 0.4665885 0.8402235
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7845519 0.8083558 0.0995997 0.7766863
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.4595635 0.795333
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7772394 0.8125808 0.3354449 3419.307
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0003600752 0.01639765# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !zeroVar &
(exclude.as.feat != 1))[, "id"]
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitent_df, OOB_df=glb_OOBent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.glm"
## [1] " indep_vars: WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, A.npnct21.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct25.log, H.has.ebola, S.make.log, H.npnct01.log, .rnorm, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, S.npnct22.log, H.npnct02.log, S.presid.log, S.npnct15.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, PubDate.minute.fctr, S.new.log, S.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, S.day.log, H.X2014.log, S.show.log, A.npnct14.log, S.report.log, S.year.log, H.npnct04.log, S.share.log, S.compani.log, H.new.log, S.first.log, S.time.log, H.newyork.log, S.articl.log, S.will.log, H.npnct15.log, S.newyork.log, H.day.log, S.npnct04.log, H.today.log, H.report.log, S.npnct16.log, S.intern.log, H.daili.log, H.week.log, H.npnct16.log, S.fashion.log, S.week.log, H.npnct30.log, S.npnct12.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, A.nchrs.log, A.nwrds.log, A.nwrds.unq.log, S.nuppr.log"
## + Fold1: parameter=none## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1: parameter=none
## + Fold2: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2: parameter=none
## + Fold3: parameter=none## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 1143, 1977, 2501, 2502, 3637, 4105, 4408
## Warning: not plotting observations with leverage one:
## 1143, 1977, 2501, 2502, 3637, 4105, 4408
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7515 -0.3148 -0.1351 0.0000 3.5187
##
## Coefficients: (15 not defined because of singularities)
## Estimate
## (Intercept) -4.260e+00
## WordCount.log 1.098e+00
## `PubDate.hour.fctr(7.67,15.3]` 8.798e-02
## `PubDate.hour.fctr(15.3,23]` 2.505e-01
## H.npnct21.log 1.506e+00
## PubDate.wkend -2.575e-01
## A.npnct21.log 1.446e+00
## H.npnct09.log 2.087e+00
## PubDate.last10.log 2.386e-01
## PubDate.last1.log -4.989e-02
## S.npnct01.log 1.950e+00
## S.can.log -7.442e-01
## H.npnct17.log 1.000e+00
## S.npnct25.log NA
## H.has.ebola -3.483e-01
## S.make.log -4.046e-01
## H.npnct01.log -1.305e+00
## .rnorm -5.272e-03
## A.npnct23.log -6.497e+15
## A.npnct25.log NA
## H.npnct12.log 4.260e-01
## `myCategory.fctrForeign#World#Asia Pacific` -4.060e+00
## `myCategory.fctr#Multimedia#` -4.337e+00
## `myCategory.fctrCulture#Arts#` -2.850e+00
## `myCategory.fctrBusiness#Business Day#Dealbook` -2.405e+00
## myCategory.fctrmyOther -1.984e+01
## `myCategory.fctrBusiness#Technology#` -1.836e+00
## `myCategory.fctrBusiness#Crosswords/Games#` 8.832e-01
## `myCategory.fctrTStyle##` -4.200e+00
## `myCategory.fctrForeign#World#` -1.213e+01
## `myCategory.fctrOpEd#Opinion#` 6.999e-01
## `myCategory.fctrStyles##Fashion` -2.669e+01
## `myCategory.fctr#Opinion#Room For Debate` -5.515e+00
## `myCategory.fctr#U.S.#Education` -2.212e+01
## `myCategory.fctr##` -2.718e+00
## `myCategory.fctrMetro#N.Y. / Region#` -1.877e+00
## `myCategory.fctrBusiness#Business Day#Small Business` -4.512e+00
## `myCategory.fctrStyles#U.S.#` -4.994e-01
## `myCategory.fctrTravel#Travel#` -4.033e+00
## `myCategory.fctr#Opinion#The Public Editor` 1.218e+00
## H.npnct03.log NA
## S.state.log 2.443e+14
## A.state.log -2.443e+14
## S.one.log 3.281e+01
## A.one.log -3.320e+01
## A.said.log 8.717e-01
## S.said.log NA
## A.npnct17.log -3.669e-01
## S.npnct17.log NA
## S.npnct08.log 1.817e+01
## A.npnct08.log NA
## S.npnct09.log -1.664e+01
## A.npnct09.log NA
## A.npnct27.log 2.443e+14
## A.npnct11.log NA
## H.npnct11.log -3.110e+01
## H.npnct22.log -1.868e+00
## S.npnct02.log -2.462e+01
## S.npnct11.log NA
## PubDate.last100.log 2.272e-02
## H.npnct05.log -3.408e+01
## `PubDate.date.fctr(7,13]` -3.726e-02
## `PubDate.date.fctr(13,19]` -1.508e-01
## `PubDate.date.fctr(19,25]` -9.796e-02
## `PubDate.date.fctr(25,31]` 1.253e-01
## `PubDate.second.fctr(14.8,29.5]` 8.011e-02
## `PubDate.second.fctr(29.5,44.2]` -1.157e-02
## `PubDate.second.fctr(44.2,59.1]` -2.946e-01
## H.npnct07.log 2.201e-01
## A.npnct07.log -3.585e+01
## S.npnct07.log NA
## S.npnct03.log -3.765e+01
## A.npnct19.log -7.406e+00
## H.npnct13.log 3.428e-01
## A.has.http NA
## A.npnct03.log NA
## A.npnct02.log NA
## A.npnct18.log 4.430e+00
## A.npnct20.log NA
## A.has.year.colon -1.906e+01
## S.has.year.colon NA
## S.npnct22.log -3.387e+01
## H.npnct02.log -2.358e+01
## S.presid.log 4.570e-01
## S.npnct15.log 1.313e+01
## S.npnct06.log 3.211e-02
## A.npnct15.log -1.231e+01
## H.npnct14.log -2.163e-01
## S.take.log -6.539e-01
## `PubDate.minute.fctr(14.8,29.5]` -1.175e-01
## `PubDate.minute.fctr(29.5,44.2]` -1.994e-01
## `PubDate.minute.fctr(44.2,59.1]` 6.085e-02
## S.new.log 1.426e-02
## S.npnct13.log -1.447e-01
## PubDate.wkday.fctr1 -5.031e-01
## PubDate.wkday.fctr2 -1.130e+00
## PubDate.wkday.fctr3 -7.418e-01
## PubDate.wkday.fctr4 -9.712e-01
## PubDate.wkday.fctr5 -8.478e-01
## PubDate.wkday.fctr6 -1.317e+00
## S.npnct30.log -2.088e+01
## S.day.log -1.872e-01
## H.X2014.log -1.054e+00
## S.show.log -6.204e-01
## A.npnct14.log 9.695e-01
## S.report.log -1.293e+00
## S.year.log -3.837e-01
## H.npnct04.log -1.874e+00
## S.share.log -9.823e-01
## S.compani.log -4.461e-01
## H.new.log -8.862e-01
## S.first.log -1.510e-01
## S.time.log -2.793e-01
## H.newyork.log 1.200e-01
## S.articl.log -1.955e-01
## S.will.log -5.070e-01
## H.npnct15.log -3.039e+01
## S.newyork.log 1.082e+00
## H.day.log -1.155e+00
## S.npnct04.log -1.149e+00
## H.today.log -3.444e+00
## H.report.log -7.682e-01
## S.npnct16.log 4.628e-01
## S.intern.log -9.615e-01
## H.daili.log -1.370e+01
## H.week.log -6.305e-01
## H.npnct16.log -2.355e-01
## S.fashion.log -3.088e+01
## S.week.log -2.581e-01
## H.npnct30.log -1.576e-01
## S.npnct12.log -1.554e-01
## H.ndgts.log 4.681e-01
## S.ndgts.log -3.416e-01
## H.nuppr.log 1.271e+00
## H.nchrs.log -9.599e-01
## H.nwrds.log -7.825e-01
## A.nchrs.log 2.540e-01
## A.nwrds.log 8.726e-01
## A.nwrds.unq.log -1.618e+00
## S.nuppr.log -6.755e-01
## Std. Error
## (Intercept) 2.125e+00
## WordCount.log 8.980e-02
## `PubDate.hour.fctr(7.67,15.3]` 2.464e-01
## `PubDate.hour.fctr(15.3,23]` 2.508e-01
## H.npnct21.log 3.163e-01
## PubDate.wkend 4.455e-01
## A.npnct21.log 3.294e-01
## H.npnct09.log 7.189e-01
## PubDate.last10.log 1.258e-01
## PubDate.last1.log 4.379e-02
## S.npnct01.log 1.748e+00
## S.can.log 4.617e-01
## H.npnct17.log 5.705e-01
## S.npnct25.log NA
## H.has.ebola 4.452e-01
## S.make.log 4.226e-01
## H.npnct01.log 1.255e+00
## .rnorm 6.323e-02
## A.npnct23.log 6.846e+07
## A.npnct25.log NA
## H.npnct12.log 2.088e-01
## `myCategory.fctrForeign#World#Asia Pacific` 6.372e-01
## `myCategory.fctr#Multimedia#` 7.849e-01
## `myCategory.fctrCulture#Arts#` 3.664e-01
## `myCategory.fctrBusiness#Business Day#Dealbook` 3.037e-01
## myCategory.fctrmyOther 1.737e+03
## `myCategory.fctrBusiness#Technology#` 3.208e-01
## `myCategory.fctrBusiness#Crosswords/Games#` 4.971e-01
## `myCategory.fctrTStyle##` 4.932e-01
## `myCategory.fctrForeign#World#` 4.026e+01
## `myCategory.fctrOpEd#Opinion#` 2.930e-01
## `myCategory.fctrStyles##Fashion` 3.319e+04
## `myCategory.fctr#Opinion#Room For Debate` 6.235e-01
## `myCategory.fctr#U.S.#Education` 1.105e+03
## `myCategory.fctr##` 2.858e-01
## `myCategory.fctrMetro#N.Y. / Region#` 4.681e-01
## `myCategory.fctrBusiness#Business Day#Small Business` 6.882e-01
## `myCategory.fctrStyles#U.S.#` 3.342e-01
## `myCategory.fctrTravel#Travel#` 1.073e+00
## `myCategory.fctr#Opinion#The Public Editor` 1.193e+00
## H.npnct03.log NA
## S.state.log 2.825e+14
## A.state.log 2.825e+14
## S.one.log 5.072e+05
## A.one.log 5.072e+05
## A.said.log 4.127e-01
## S.said.log NA
## A.npnct17.log 1.288e+00
## S.npnct17.log NA
## S.npnct08.log 2.523e+05
## A.npnct08.log NA
## S.npnct09.log 2.523e+05
## A.npnct09.log NA
## A.npnct27.log 2.825e+14
## A.npnct11.log NA
## H.npnct11.log 4.926e+05
## H.npnct22.log 5.145e+05
## S.npnct02.log 3.321e+05
## S.npnct11.log NA
## PubDate.last100.log 4.592e-02
## H.npnct05.log 1.920e+05
## `PubDate.date.fctr(7,13]` 1.952e-01
## `PubDate.date.fctr(13,19]` 1.930e-01
## `PubDate.date.fctr(19,25]` 1.898e-01
## `PubDate.date.fctr(25,31]` 2.031e-01
## `PubDate.second.fctr(14.8,29.5]` 1.728e-01
## `PubDate.second.fctr(29.5,44.2]` 1.697e-01
## `PubDate.second.fctr(44.2,59.1]` 1.771e-01
## H.npnct07.log 1.852e-01
## A.npnct07.log 2.252e+05
## S.npnct07.log NA
## S.npnct03.log 1.704e+05
## A.npnct19.log 3.311e+06
## H.npnct13.log 3.097e-01
## A.has.http NA
## A.npnct03.log NA
## A.npnct02.log NA
## A.npnct18.log 1.127e+06
## A.npnct20.log NA
## A.has.year.colon 6.842e+04
## S.has.year.colon NA
## S.npnct22.log 1.436e+05
## H.npnct02.log 9.238e+04
## S.presid.log 4.670e-01
## S.npnct15.log 1.786e+06
## S.npnct06.log 1.520e+00
## A.npnct15.log 1.786e+06
## H.npnct14.log 1.961e-01
## S.take.log 5.543e-01
## `PubDate.minute.fctr(14.8,29.5]` 1.809e-01
## `PubDate.minute.fctr(29.5,44.2]` 1.747e-01
## `PubDate.minute.fctr(44.2,59.1]` 1.815e-01
## S.new.log 3.091e-01
## S.npnct13.log 1.981e-01
## PubDate.wkday.fctr1 5.211e-01
## PubDate.wkday.fctr2 5.670e-01
## PubDate.wkday.fctr3 5.595e-01
## PubDate.wkday.fctr4 5.530e-01
## PubDate.wkday.fctr5 5.593e-01
## PubDate.wkday.fctr6 4.646e-01
## S.npnct30.log 3.906e+04
## S.day.log 6.270e-01
## H.X2014.log 1.435e+00
## S.show.log 6.103e-01
## A.npnct14.log 2.615e-01
## S.report.log 6.076e-01
## S.year.log 4.567e-01
## H.npnct04.log 9.402e-01
## S.share.log 6.562e-01
## S.compani.log 4.137e-01
## H.new.log 6.220e-01
## S.first.log 6.227e-01
## S.time.log 4.609e-01
## H.newyork.log 7.020e-01
## S.articl.log 1.147e+00
## S.will.log 3.714e-01
## H.npnct15.log 4.316e+04
## S.newyork.log 5.143e-01
## H.day.log 1.044e+00
## S.npnct04.log 6.879e-01
## H.today.log 9.766e-01
## H.report.log 1.007e+00
## S.npnct16.log 4.770e-01
## S.intern.log 1.208e+00
## H.daili.log 1.122e+02
## H.week.log 9.343e-01
## H.npnct16.log 2.848e-01
## S.fashion.log 2.957e+04
## S.week.log 4.751e-01
## H.npnct30.log 1.714e+00
## S.npnct12.log 1.429e-01
## H.ndgts.log 2.474e-01
## S.ndgts.log 1.543e-01
## H.nuppr.log 4.208e-01
## H.nchrs.log 4.345e-01
## H.nwrds.log 4.444e-01
## A.nchrs.log 5.061e-01
## A.nwrds.log 1.647e+00
## A.nwrds.unq.log 1.587e+00
## S.nuppr.log 1.558e-01
## z value Pr(>|z|)
## (Intercept) -2.004e+00 0.045039
## WordCount.log 1.223e+01 < 2e-16
## `PubDate.hour.fctr(7.67,15.3]` 3.570e-01 0.721020
## `PubDate.hour.fctr(15.3,23]` 9.990e-01 0.317879
## H.npnct21.log 4.763e+00 1.91e-06
## PubDate.wkend -5.780e-01 0.563328
## A.npnct21.log 4.390e+00 1.13e-05
## H.npnct09.log 2.903e+00 0.003692
## PubDate.last10.log 1.896e+00 0.057944
## PubDate.last1.log -1.139e+00 0.254600
## S.npnct01.log 1.116e+00 0.264568
## S.can.log -1.612e+00 0.107046
## H.npnct17.log 1.753e+00 0.079565
## S.npnct25.log NA NA
## H.has.ebola -7.820e-01 0.434109
## S.make.log -9.570e-01 0.338350
## H.npnct01.log -1.040e+00 0.298319
## .rnorm -8.300e-02 0.933553
## A.npnct23.log -9.491e+07 < 2e-16
## A.npnct25.log NA NA
## H.npnct12.log 2.040e+00 0.041372
## `myCategory.fctrForeign#World#Asia Pacific` -6.372e+00 1.87e-10
## `myCategory.fctr#Multimedia#` -5.525e+00 3.30e-08
## `myCategory.fctrCulture#Arts#` -7.778e+00 7.37e-15
## `myCategory.fctrBusiness#Business Day#Dealbook` -7.920e+00 2.37e-15
## myCategory.fctrmyOther -1.100e-02 0.990884
## `myCategory.fctrBusiness#Technology#` -5.724e+00 1.04e-08
## `myCategory.fctrBusiness#Crosswords/Games#` 1.777e+00 0.075618
## `myCategory.fctrTStyle##` -8.516e+00 < 2e-16
## `myCategory.fctrForeign#World#` -3.010e-01 0.763229
## `myCategory.fctrOpEd#Opinion#` 2.389e+00 0.016888
## `myCategory.fctrStyles##Fashion` -1.000e-03 0.999358
## `myCategory.fctr#Opinion#Room For Debate` -8.846e+00 < 2e-16
## `myCategory.fctr#U.S.#Education` -2.000e-02 0.984027
## `myCategory.fctr##` -9.512e+00 < 2e-16
## `myCategory.fctrMetro#N.Y. / Region#` -4.010e+00 6.07e-05
## `myCategory.fctrBusiness#Business Day#Small Business` -6.556e+00 5.51e-11
## `myCategory.fctrStyles#U.S.#` -1.494e+00 0.135116
## `myCategory.fctrTravel#Travel#` -3.760e+00 0.000170
## `myCategory.fctr#Opinion#The Public Editor` 1.021e+00 0.307391
## H.npnct03.log NA NA
## S.state.log 8.650e-01 0.387232
## A.state.log -8.650e-01 0.387232
## S.one.log 0.000e+00 0.999948
## A.one.log 0.000e+00 0.999948
## A.said.log 2.112e+00 0.034659
## S.said.log NA NA
## A.npnct17.log -2.850e-01 0.775847
## S.npnct17.log NA NA
## S.npnct08.log 0.000e+00 0.999943
## A.npnct08.log NA NA
## S.npnct09.log 0.000e+00 0.999947
## A.npnct09.log NA NA
## A.npnct27.log 8.650e-01 0.387232
## A.npnct11.log NA NA
## H.npnct11.log 0.000e+00 0.999950
## H.npnct22.log 0.000e+00 0.999997
## S.npnct02.log 0.000e+00 0.999941
## S.npnct11.log NA NA
## PubDate.last100.log 4.950e-01 0.620815
## H.npnct05.log 0.000e+00 0.999858
## `PubDate.date.fctr(7,13]` -1.910e-01 0.848639
## `PubDate.date.fctr(13,19]` -7.810e-01 0.434583
## `PubDate.date.fctr(19,25]` -5.160e-01 0.605781
## `PubDate.date.fctr(25,31]` 6.170e-01 0.537249
## `PubDate.second.fctr(14.8,29.5]` 4.640e-01 0.642967
## `PubDate.second.fctr(29.5,44.2]` -6.800e-02 0.945649
## `PubDate.second.fctr(44.2,59.1]` -1.663e+00 0.096295
## H.npnct07.log 1.188e+00 0.234838
## A.npnct07.log 0.000e+00 0.999873
## S.npnct07.log NA NA
## S.npnct03.log 0.000e+00 0.999824
## A.npnct19.log 0.000e+00 0.999998
## H.npnct13.log 1.107e+00 0.268403
## A.has.http NA NA
## A.npnct03.log NA NA
## A.npnct02.log NA NA
## A.npnct18.log 0.000e+00 0.999997
## A.npnct20.log NA NA
## A.has.year.colon 0.000e+00 0.999778
## S.has.year.colon NA NA
## S.npnct22.log 0.000e+00 0.999812
## H.npnct02.log 0.000e+00 0.999796
## S.presid.log 9.790e-01 0.327731
## S.npnct15.log 0.000e+00 0.999994
## S.npnct06.log 2.100e-02 0.983146
## A.npnct15.log 0.000e+00 0.999995
## H.npnct14.log -1.103e+00 0.270021
## S.take.log -1.180e+00 0.238180
## `PubDate.minute.fctr(14.8,29.5]` -6.490e-01 0.516016
## `PubDate.minute.fctr(29.5,44.2]` -1.141e+00 0.253727
## `PubDate.minute.fctr(44.2,59.1]` 3.350e-01 0.737411
## S.new.log 4.600e-02 0.963189
## S.npnct13.log -7.300e-01 0.465188
## PubDate.wkday.fctr1 -9.650e-01 0.334319
## PubDate.wkday.fctr2 -1.993e+00 0.046307
## PubDate.wkday.fctr3 -1.326e+00 0.184906
## PubDate.wkday.fctr4 -1.756e+00 0.079020
## PubDate.wkday.fctr5 -1.516e+00 0.129572
## PubDate.wkday.fctr6 -2.834e+00 0.004598
## S.npnct30.log -1.000e-03 0.999573
## S.day.log -2.990e-01 0.765265
## H.X2014.log -7.350e-01 0.462577
## S.show.log -1.017e+00 0.309388
## A.npnct14.log 3.707e+00 0.000209
## S.report.log -2.128e+00 0.033373
## S.year.log -8.400e-01 0.400871
## H.npnct04.log -1.994e+00 0.046206
## S.share.log -1.497e+00 0.134392
## S.compani.log -1.078e+00 0.280906
## H.new.log -1.425e+00 0.154226
## S.first.log -2.430e-01 0.808364
## S.time.log -6.060e-01 0.544586
## H.newyork.log 1.710e-01 0.864293
## S.articl.log -1.700e-01 0.864648
## S.will.log -1.365e+00 0.172295
## H.npnct15.log -1.000e-03 0.999438
## S.newyork.log 2.103e+00 0.035459
## H.day.log -1.106e+00 0.268689
## S.npnct04.log -1.670e+00 0.094837
## H.today.log -3.527e+00 0.000421
## H.report.log -7.630e-01 0.445472
## S.npnct16.log 9.700e-01 0.331910
## S.intern.log -7.960e-01 0.426005
## H.daili.log -1.220e-01 0.902775
## H.week.log -6.750e-01 0.499793
## H.npnct16.log -8.270e-01 0.408408
## S.fashion.log -1.000e-03 0.999167
## S.week.log -5.430e-01 0.586991
## H.npnct30.log -9.200e-02 0.926752
## S.npnct12.log -1.088e+00 0.276810
## H.ndgts.log 1.892e+00 0.058486
## S.ndgts.log -2.214e+00 0.026796
## H.nuppr.log 3.020e+00 0.002528
## H.nchrs.log -2.209e+00 0.027161
## H.nwrds.log -1.761e+00 0.078289
## A.nchrs.log 5.020e-01 0.615783
## A.nwrds.log 5.300e-01 0.596341
## A.nwrds.unq.log -1.019e+00 0.308053
## S.nuppr.log -4.337e+00 1.45e-05
##
## (Intercept) *
## WordCount.log ***
## `PubDate.hour.fctr(7.67,15.3]`
## `PubDate.hour.fctr(15.3,23]`
## H.npnct21.log ***
## PubDate.wkend
## A.npnct21.log ***
## H.npnct09.log **
## PubDate.last10.log .
## PubDate.last1.log
## S.npnct01.log
## S.can.log
## H.npnct17.log .
## S.npnct25.log
## H.has.ebola
## S.make.log
## H.npnct01.log
## .rnorm
## A.npnct23.log ***
## A.npnct25.log
## H.npnct12.log *
## `myCategory.fctrForeign#World#Asia Pacific` ***
## `myCategory.fctr#Multimedia#` ***
## `myCategory.fctrCulture#Arts#` ***
## `myCategory.fctrBusiness#Business Day#Dealbook` ***
## myCategory.fctrmyOther
## `myCategory.fctrBusiness#Technology#` ***
## `myCategory.fctrBusiness#Crosswords/Games#` .
## `myCategory.fctrTStyle##` ***
## `myCategory.fctrForeign#World#`
## `myCategory.fctrOpEd#Opinion#` *
## `myCategory.fctrStyles##Fashion`
## `myCategory.fctr#Opinion#Room For Debate` ***
## `myCategory.fctr#U.S.#Education`
## `myCategory.fctr##` ***
## `myCategory.fctrMetro#N.Y. / Region#` ***
## `myCategory.fctrBusiness#Business Day#Small Business` ***
## `myCategory.fctrStyles#U.S.#`
## `myCategory.fctrTravel#Travel#` ***
## `myCategory.fctr#Opinion#The Public Editor`
## H.npnct03.log
## S.state.log
## A.state.log
## S.one.log
## A.one.log
## A.said.log *
## S.said.log
## A.npnct17.log
## S.npnct17.log
## S.npnct08.log
## A.npnct08.log
## S.npnct09.log
## A.npnct09.log
## A.npnct27.log
## A.npnct11.log
## H.npnct11.log
## H.npnct22.log
## S.npnct02.log
## S.npnct11.log
## PubDate.last100.log
## H.npnct05.log
## `PubDate.date.fctr(7,13]`
## `PubDate.date.fctr(13,19]`
## `PubDate.date.fctr(19,25]`
## `PubDate.date.fctr(25,31]`
## `PubDate.second.fctr(14.8,29.5]`
## `PubDate.second.fctr(29.5,44.2]`
## `PubDate.second.fctr(44.2,59.1]` .
## H.npnct07.log
## A.npnct07.log
## S.npnct07.log
## S.npnct03.log
## A.npnct19.log
## H.npnct13.log
## A.has.http
## A.npnct03.log
## A.npnct02.log
## A.npnct18.log
## A.npnct20.log
## A.has.year.colon
## S.has.year.colon
## S.npnct22.log
## H.npnct02.log
## S.presid.log
## S.npnct15.log
## S.npnct06.log
## A.npnct15.log
## H.npnct14.log
## S.take.log
## `PubDate.minute.fctr(14.8,29.5]`
## `PubDate.minute.fctr(29.5,44.2]`
## `PubDate.minute.fctr(44.2,59.1]`
## S.new.log
## S.npnct13.log
## PubDate.wkday.fctr1
## PubDate.wkday.fctr2 *
## PubDate.wkday.fctr3
## PubDate.wkday.fctr4 .
## PubDate.wkday.fctr5
## PubDate.wkday.fctr6 **
## S.npnct30.log
## S.day.log
## H.X2014.log
## S.show.log
## A.npnct14.log ***
## S.report.log *
## S.year.log
## H.npnct04.log *
## S.share.log
## S.compani.log
## H.new.log
## S.first.log
## S.time.log
## H.newyork.log
## S.articl.log
## S.will.log
## H.npnct15.log
## S.newyork.log *
## H.day.log
## S.npnct04.log .
## H.today.log ***
## H.report.log
## S.npnct16.log
## S.intern.log
## H.daili.log
## H.week.log
## H.npnct16.log
## S.fashion.log
## S.week.log
## H.npnct30.log
## S.npnct12.log
## H.ndgts.log .
## S.ndgts.log *
## H.nuppr.log **
## H.nchrs.log *
## H.nwrds.log .
## A.nchrs.log
## A.nwrds.log
## A.nwrds.unq.log
## S.nuppr.log ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1842.9 on 4350 degrees of freedom
## AIC: 2092.9
##
## Number of Fisher Scoring iterations: 25
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.6616400
## 3 0.2 0.7321226
## 4 0.3 0.7441860
## 5 0.4 0.7373396
## 6 0.5 0.7324750
## 7 0.6 0.7091875
## 8 0.7 0.6595570
## 9 0.8 0.5698730
## 10 0.9 0.3744681
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Low.cor.X.glm.N
## 1 N 3476
## 2 Y 157
## Popular.fctr.predict.Low.cor.X.glm.Y
## 1 250
## 2 592
## Prediction
## Reference N Y
## N 3476 250
## Y 157 592
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.090503e-01 6.891093e-01 9.002448e-01 9.173176e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 1.896445e-49 5.108772e-06
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.6359833
## 3 0.2 0.7048568
## 4 0.3 0.7252747
## 5 0.4 0.7088235
## 6 0.5 0.6990596
## 7 0.6 0.6644068
## 8 0.7 0.6162162
## 9 0.8 0.5461690
## 10 0.9 0.3317536
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Low.cor.X.glm.N
## 1 N 1593
## 2 Y 80
## Popular.fctr.predict.Low.cor.X.glm.Y
## 1 120
## 2 264
## Prediction
## Reference N Y
## N 1593 120
## Y 80 264
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.027710e-01 6.664245e-01 8.891448e-01 9.152364e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 6.489691e-20 5.820666e-03
## model_id model_method
## 1 Low.cor.X.glm glm
## feats
## 1 WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, A.npnct21.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct25.log, H.has.ebola, S.make.log, H.npnct01.log, .rnorm, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, S.npnct22.log, H.npnct02.log, S.presid.log, S.npnct15.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, PubDate.minute.fctr, S.new.log, S.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, S.day.log, H.X2014.log, S.show.log, A.npnct14.log, S.report.log, S.year.log, H.npnct04.log, S.share.log, S.compani.log, H.new.log, S.first.log, S.time.log, H.newyork.log, S.articl.log, S.will.log, H.npnct15.log, S.newyork.log, H.day.log, S.npnct04.log, H.today.log, H.report.log, S.npnct16.log, S.intern.log, H.daili.log, H.week.log, H.npnct16.log, S.fashion.log, S.week.log, H.npnct30.log, S.npnct12.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, A.nchrs.log, A.nwrds.log, A.nwrds.unq.log, S.nuppr.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 14.959 4.685
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9487497 0.3 0.744186 0.8929586
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9002448 0.9173176 0.606769 0.9226367
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7252747 0.902771
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.8891448 0.9152364 0.6664245 2092.942
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01556256 0.04242306glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 9 fit.models 6 0 176.477 222.29 45.814
## 10 fit.models 6 1 222.291 NA NA# All X that is not user excluded
# if (glb_is_classification && glb_is_binomial) {
# model_id_pfx <- "Conditional.X"
# # indep_vars_vctr <- setdiff(names(glb_fitent_df), union(glb_rsp_var, glb_exclude_vars_as_features))
# indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"]
# } else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !zeroVar &
(exclude.as.feat != 1))[, "id"]
# }
for (method in glb_models_method_vctr) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitent_df, OOB_df=glb_OOBent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitent_df, OOB_df=glb_OOBent_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}## [1] "fitting model: All.X.glm"
## [1] " indep_vars: WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, S.npnct21.log, A.npnct21.log, H.npnct08.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, H.npnct06.log, A.can.log, A.npnct01.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct23.log, S.npnct25.log, H.has.ebola, A.make.log, S.make.log, H.npnct01.log, .rnorm, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, A.npnct22.log, S.npnct22.log, H.npnct02.log, A.presid.log, S.presid.log, S.npnct15.log, A.npnct06.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, A.take.log, PubDate.minute.fctr, S.new.log, A.new.log, S.npnct13.log, A.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, A.npnct30.log, S.day.log, A.day.log, H.X2014.log, A.show.log, S.show.log, A.npnct14.log, A.report.log, S.report.log, A.year.log, S.year.log, H.npnct04.log, A.share.log, S.share.log, S.compani.log, A.compani.log, H.new.log, S.npnct14.log, A.first.log, S.first.log, S.time.log, A.time.log, H.newyork.log, A.articl.log, S.articl.log, S.will.log, A.will.log, H.npnct15.log, A.newyork.log, S.newyork.log, H.day.log, A.npnct04.log, S.npnct04.log, H.today.log, H.report.log, H.X2015.log, S.npnct16.log, A.intern.log, S.intern.log, A.npnct16.log, H.daili.log, H.week.log, H.has.year.colon, H.fashion.log, H.npnct16.log, A.fashion.log, S.fashion.log, A.week.log, S.week.log, H.npnct30.log, S.npnct12.log, A.npnct12.log, H.ndgts.log, S.ndgts.log, A.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, H.nwrds.unq.log, A.nchrs.log, S.nchrs.log, A.nwrds.log, S.nwrds.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, A.nuppr.log"
## + Fold1: parameter=none## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1: parameter=none
## + Fold2: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2: parameter=none
## + Fold3: parameter=none## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 1143, 1161, 1930, 1977, 2502, 3799, 4105, 4408
## Warning: not plotting observations with leverage one:
## 1143, 1161, 1930, 1977, 2502, 3799, 4105, 4408
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.49 0.00 0.00 0.00 8.49
##
## Coefficients: (39 not defined because of singularities)
## Estimate
## (Intercept) 7.336e+14
## WordCount.log 2.451e+14
## `PubDate.hour.fctr(7.67,15.3]` 3.403e+13
## `PubDate.hour.fctr(15.3,23]` 5.287e+13
## H.npnct21.log 9.165e+14
## PubDate.wkend -1.327e+13
## S.npnct21.log 2.942e+15
## A.npnct21.log -2.126e+15
## H.npnct08.log -8.494e+14
## H.npnct09.log NA
## PubDate.last10.log 1.204e+14
## PubDate.last1.log -5.490e+12
## H.npnct06.log -5.766e+14
## A.can.log 8.075e+15
## A.npnct01.log 7.136e+13
## S.npnct01.log NA
## S.can.log -8.263e+15
## H.npnct17.log -1.263e+14
## S.npnct23.log NA
## S.npnct25.log NA
## H.has.ebola -7.683e+14
## A.make.log -6.327e+14
## S.make.log NA
## H.npnct01.log -8.337e+14
## .rnorm 9.930e+13
## A.npnct23.log -1.771e+15
## A.npnct25.log NA
## H.npnct12.log 1.903e+14
## `myCategory.fctrForeign#World#Asia Pacific` -2.822e+15
## `myCategory.fctr#Multimedia#` -3.744e+15
## `myCategory.fctrCulture#Arts#` -1.878e+15
## `myCategory.fctrBusiness#Business Day#Dealbook` -4.884e+15
## myCategory.fctrmyOther -5.716e+15
## `myCategory.fctrBusiness#Technology#` -2.390e+15
## `myCategory.fctrBusiness#Crosswords/Games#` -8.911e+14
## `myCategory.fctrTStyle##` -2.217e+15
## `myCategory.fctrForeign#World#` -3.782e+15
## `myCategory.fctrOpEd#Opinion#` 5.803e+14
## `myCategory.fctrStyles##Fashion` -5.396e+15
## `myCategory.fctr#Opinion#Room For Debate` -4.318e+15
## `myCategory.fctr#U.S.#Education` -6.978e+15
## `myCategory.fctr##` -4.691e+15
## `myCategory.fctrMetro#N.Y. / Region#` -1.945e+15
## `myCategory.fctrBusiness#Business Day#Small Business` -5.715e+15
## `myCategory.fctrStyles#U.S.#` -1.121e+15
## `myCategory.fctrTravel#Travel#` -2.560e+15
## `myCategory.fctr#Opinion#The Public Editor` -8.298e+13
## H.npnct03.log NA
## S.state.log 1.009e+16
## A.state.log -9.680e+15
## S.one.log 1.413e+15
## A.one.log -2.247e+15
## A.said.log 1.068e+15
## S.said.log NA
## A.npnct17.log -4.378e+14
## S.npnct17.log NA
## S.npnct08.log 1.870e+15
## A.npnct08.log NA
## S.npnct09.log -2.851e+15
## A.npnct09.log NA
## A.npnct27.log NA
## A.npnct11.log NA
## H.npnct11.log -5.274e+15
## H.npnct22.log 1.817e+15
## S.npnct02.log 3.057e+14
## S.npnct11.log NA
## PubDate.last100.log 1.286e+13
## H.npnct05.log -2.880e+15
## `PubDate.date.fctr(7,13]` -9.212e+13
## `PubDate.date.fctr(13,19]` 7.976e+13
## `PubDate.date.fctr(19,25]` 5.690e+13
## `PubDate.date.fctr(25,31]` 6.455e+13
## `PubDate.second.fctr(14.8,29.5]` 8.535e+13
## `PubDate.second.fctr(29.5,44.2]` 3.958e+13
## `PubDate.second.fctr(44.2,59.1]` 1.699e+13
## H.npnct07.log 3.850e+13
## A.npnct07.log 5.995e+14
## S.npnct07.log NA
## S.npnct03.log -3.845e+14
## A.npnct19.log 1.756e+16
## H.npnct13.log -5.368e+13
## A.has.http NA
## A.npnct03.log NA
## A.npnct02.log NA
## A.npnct18.log 1.421e+16
## A.npnct20.log NA
## A.has.year.colon 2.132e+15
## S.has.year.colon NA
## A.npnct22.log -3.107e+14
## S.npnct22.log NA
## H.npnct02.log -3.292e+15
## A.presid.log 6.151e+12
## S.presid.log NA
## S.npnct15.log 1.553e+16
## A.npnct06.log -4.037e+14
## S.npnct06.log NA
## A.npnct15.log -1.555e+16
## H.npnct14.log -1.160e+14
## S.take.log -2.442e+14
## A.take.log NA
## `PubDate.minute.fctr(14.8,29.5]` 6.319e+12
## `PubDate.minute.fctr(29.5,44.2]` -6.467e+13
## `PubDate.minute.fctr(44.2,59.1]` 9.576e+13
## S.new.log -1.018e+16
## A.new.log 1.005e+16
## S.npnct13.log -1.669e+15
## A.npnct13.log 1.531e+15
## PubDate.wkday.fctr1 -3.192e+13
## PubDate.wkday.fctr2 -1.959e+14
## PubDate.wkday.fctr3 -6.961e+13
## PubDate.wkday.fctr4 -1.193e+14
## PubDate.wkday.fctr5 -1.724e+14
## PubDate.wkday.fctr6 -4.728e+14
## S.npnct30.log -8.008e+15
## A.npnct30.log 7.291e+15
## S.day.log -2.167e+14
## A.day.log NA
## H.X2014.log -9.315e+14
## A.show.log -2.186e+15
## S.show.log NA
## A.npnct14.log -2.217e+14
## A.report.log -2.255e+15
## S.report.log NA
## A.year.log -6.487e+13
## S.year.log NA
## H.npnct04.log -1.582e+15
## A.share.log -2.466e+14
## S.share.log NA
## S.compani.log -2.412e+14
## A.compani.log NA
## H.new.log -1.729e+14
## S.npnct14.log 5.120e+14
## A.first.log 5.994e+13
## S.first.log NA
## S.time.log -5.285e+14
## A.time.log NA
## H.newyork.log -4.387e+14
## A.articl.log 1.711e+15
## S.articl.log NA
## S.will.log 2.070e+15
## A.will.log -2.110e+15
## H.npnct15.log 6.959e+14
## A.newyork.log -6.920e+11
## S.newyork.log NA
## H.day.log 3.221e+14
## A.npnct04.log -6.576e+13
## S.npnct04.log NA
## H.today.log -1.889e+14
## H.report.log -3.954e+14
## H.X2015.log -6.346e+14
## S.npnct16.log 3.641e+14
## A.intern.log -1.238e+15
## S.intern.log NA
## A.npnct16.log NA
## H.daili.log -2.108e+15
## H.week.log 2.697e+14
## H.has.year.colon -1.635e+15
## H.fashion.log -7.047e+13
## H.npnct16.log -1.592e+14
## A.fashion.log -1.179e+15
## S.fashion.log NA
## A.week.log -3.541e+14
## S.week.log NA
## H.npnct30.log 5.016e+14
## S.npnct12.log 2.885e+15
## A.npnct12.log -2.892e+15
## H.ndgts.log 2.919e+14
## S.ndgts.log 7.656e+14
## A.ndgts.log -9.231e+14
## H.nuppr.log 4.150e+14
## H.nchrs.log -1.509e+14
## H.nwrds.log 8.574e+13
## H.nwrds.unq.log -4.910e+14
## A.nchrs.log 1.325e+16
## S.nchrs.log -1.355e+16
## A.nwrds.log -2.684e+15
## S.nwrds.log 3.945e+15
## A.nwrds.unq.log -1.284e+16
## S.nwrds.unq.log 1.170e+16
## S.nuppr.log -4.983e+14
## A.nuppr.log 2.929e+14
## Std. Error
## (Intercept) 3.560e+07
## WordCount.log 1.213e+06
## `PubDate.hour.fctr(7.67,15.3]` 3.799e+06
## `PubDate.hour.fctr(15.3,23]` 4.045e+06
## H.npnct21.log 6.619e+06
## PubDate.wkend 7.771e+06
## S.npnct21.log 1.000e+08
## A.npnct21.log 9.996e+07
## H.npnct08.log 1.489e+07
## H.npnct09.log NA
## PubDate.last10.log 1.955e+06
## PubDate.last1.log 7.095e+05
## H.npnct06.log 1.670e+07
## A.can.log 2.064e+08
## A.npnct01.log 2.678e+07
## S.npnct01.log NA
## S.can.log 2.070e+08
## H.npnct17.log 1.784e+07
## S.npnct23.log NA
## S.npnct25.log NA
## H.has.ebola 8.879e+06
## A.make.log 7.668e+06
## S.make.log NA
## H.npnct01.log 2.340e+07
## .rnorm 1.029e+06
## A.npnct23.log 1.067e+08
## A.npnct25.log NA
## H.npnct12.log 3.660e+06
## `myCategory.fctrForeign#World#Asia Pacific` 8.779e+06
## `myCategory.fctr#Multimedia#` 1.019e+07
## `myCategory.fctrCulture#Arts#` 7.372e+06
## `myCategory.fctrBusiness#Business Day#Dealbook` 7.185e+06
## myCategory.fctrmyOther 1.513e+07
## `myCategory.fctrBusiness#Technology#` 7.951e+06
## `myCategory.fctrBusiness#Crosswords/Games#` 1.048e+07
## `myCategory.fctrTStyle##` 7.322e+06
## `myCategory.fctrForeign#World#` 1.736e+07
## `myCategory.fctrOpEd#Opinion#` 7.379e+06
## `myCategory.fctrStyles##Fashion` 1.128e+07
## `myCategory.fctr#Opinion#Room For Debate` 1.182e+07
## `myCategory.fctr#U.S.#Education` 1.138e+07
## `myCategory.fctr##` 6.802e+06
## `myCategory.fctrMetro#N.Y. / Region#` 9.716e+06
## `myCategory.fctrBusiness#Business Day#Small Business` 9.910e+06
## `myCategory.fctrStyles#U.S.#` 8.961e+06
## `myCategory.fctrTravel#Travel#` 9.799e+06
## `myCategory.fctr#Opinion#The Public Editor` 2.251e+07
## H.npnct03.log NA
## S.state.log 3.377e+08
## A.state.log 3.377e+08
## S.one.log 1.684e+08
## A.one.log 1.686e+08
## A.said.log 7.939e+06
## S.said.log NA
## A.npnct17.log 2.100e+07
## S.npnct17.log NA
## S.npnct08.log 5.208e+07
## A.npnct08.log NA
## S.npnct09.log 4.870e+07
## A.npnct09.log NA
## A.npnct27.log NA
## A.npnct11.log NA
## H.npnct11.log 9.754e+07
## H.npnct22.log 9.804e+07
## S.npnct02.log 6.299e+07
## S.npnct11.log NA
## PubDate.last100.log 7.784e+05
## H.npnct05.log 4.048e+07
## `PubDate.date.fctr(7,13]` 3.233e+06
## `PubDate.date.fctr(13,19]` 3.188e+06
## `PubDate.date.fctr(19,25]` 3.080e+06
## `PubDate.date.fctr(25,31]` 3.433e+06
## `PubDate.second.fctr(14.8,29.5]` 2.861e+06
## `PubDate.second.fctr(29.5,44.2]` 2.824e+06
## `PubDate.second.fctr(44.2,59.1]` 2.884e+06
## H.npnct07.log 2.983e+06
## A.npnct07.log 5.017e+07
## S.npnct07.log NA
## S.npnct03.log 4.404e+07
## A.npnct19.log 7.229e+08
## H.npnct13.log 5.232e+06
## A.has.http NA
## A.npnct03.log NA
## A.npnct02.log NA
## A.npnct18.log 2.206e+08
## A.npnct20.log NA
## A.has.year.colon 2.385e+07
## S.has.year.colon NA
## A.npnct22.log 3.172e+07
## S.npnct22.log NA
## H.npnct02.log 2.532e+07
## A.presid.log 7.652e+06
## S.presid.log NA
## S.npnct15.log 3.391e+08
## A.npnct06.log 1.791e+07
## S.npnct06.log NA
## A.npnct15.log 3.386e+08
## H.npnct14.log 3.444e+06
## S.take.log 8.390e+06
## A.take.log NA
## `PubDate.minute.fctr(14.8,29.5]` 2.938e+06
## `PubDate.minute.fctr(29.5,44.2]` 2.777e+06
## `PubDate.minute.fctr(44.2,59.1]` 3.000e+06
## S.new.log 1.828e+08
## A.new.log 1.826e+08
## S.npnct13.log 4.887e+07
## A.npnct13.log 4.872e+07
## PubDate.wkday.fctr1 9.447e+06
## PubDate.wkday.fctr2 1.006e+07
## PubDate.wkday.fctr3 1.000e+07
## PubDate.wkday.fctr4 9.856e+06
## PubDate.wkday.fctr5 9.983e+06
## PubDate.wkday.fctr6 7.819e+06
## S.npnct30.log 2.072e+08
## A.npnct30.log 2.057e+08
## S.day.log 8.908e+06
## A.day.log NA
## H.X2014.log 1.621e+07
## A.show.log 8.302e+06
## S.show.log NA
## A.npnct14.log 2.902e+07
## A.report.log 9.145e+06
## S.report.log NA
## A.year.log 6.827e+06
## S.year.log NA
## H.npnct04.log 1.191e+07
## A.share.log 8.764e+06
## S.share.log NA
## S.compani.log 6.665e+06
## A.compani.log NA
## H.new.log 8.032e+06
## S.npnct14.log 2.879e+07
## A.first.log 8.267e+06
## S.first.log NA
## S.time.log 6.815e+06
## A.time.log NA
## H.newyork.log 9.798e+06
## A.articl.log 1.200e+07
## S.articl.log NA
## S.will.log 1.030e+08
## A.will.log 1.030e+08
## H.npnct15.log 2.330e+07
## A.newyork.log 7.217e+06
## S.newyork.log NA
## H.day.log 1.027e+07
## A.npnct04.log 8.060e+06
## S.npnct04.log NA
## H.today.log 1.208e+07
## H.report.log 1.258e+07
## H.X2015.log 2.467e+07
## S.npnct16.log 8.190e+06
## A.intern.log 1.492e+07
## S.intern.log NA
## A.npnct16.log NA
## H.daili.log 1.584e+07
## H.week.log 1.320e+07
## H.has.year.colon 1.430e+07
## H.fashion.log 1.501e+07
## H.npnct16.log 4.811e+06
## A.fashion.log 1.143e+07
## S.fashion.log NA
## A.week.log 6.941e+06
## S.week.log NA
## H.npnct30.log 1.470e+07
## S.npnct12.log 1.801e+08
## A.npnct12.log 1.801e+08
## H.ndgts.log 4.115e+06
## S.ndgts.log 3.682e+07
## A.ndgts.log 3.671e+07
## H.nuppr.log 7.531e+06
## H.nchrs.log 7.534e+06
## H.nwrds.log 3.855e+07
## H.nwrds.unq.log 3.788e+07
## A.nchrs.log 4.543e+08
## S.nchrs.log 4.542e+08
## A.nwrds.log 7.379e+08
## S.nwrds.log 7.380e+08
## A.nwrds.unq.log 5.948e+08
## S.nwrds.unq.log 5.947e+08
## S.nuppr.log 1.056e+08
## A.nuppr.log 1.056e+08
## z value Pr(>|z|)
## (Intercept) 20607958 <2e-16
## WordCount.log 201958080 <2e-16
## `PubDate.hour.fctr(7.67,15.3]` 8958289 <2e-16
## `PubDate.hour.fctr(15.3,23]` 13072790 <2e-16
## H.npnct21.log 138468093 <2e-16
## PubDate.wkend -1707155 <2e-16
## S.npnct21.log 29411471 <2e-16
## A.npnct21.log -21271434 <2e-16
## H.npnct08.log -57034642 <2e-16
## H.npnct09.log NA NA
## PubDate.last10.log 61594005 <2e-16
## PubDate.last1.log -7738034 <2e-16
## H.npnct06.log -34534529 <2e-16
## A.can.log 39122525 <2e-16
## A.npnct01.log 2664432 <2e-16
## S.npnct01.log NA NA
## S.can.log -39924010 <2e-16
## H.npnct17.log -7079590 <2e-16
## S.npnct23.log NA NA
## S.npnct25.log NA NA
## H.has.ebola -86529385 <2e-16
## A.make.log -82520227 <2e-16
## S.make.log NA NA
## H.npnct01.log -35631842 <2e-16
## .rnorm 96525806 <2e-16
## A.npnct23.log -16592828 <2e-16
## A.npnct25.log NA NA
## H.npnct12.log 51989973 <2e-16
## `myCategory.fctrForeign#World#Asia Pacific` -321385613 <2e-16
## `myCategory.fctr#Multimedia#` -367616241 <2e-16
## `myCategory.fctrCulture#Arts#` -254689231 <2e-16
## `myCategory.fctrBusiness#Business Day#Dealbook` -679753902 <2e-16
## myCategory.fctrmyOther -377736326 <2e-16
## `myCategory.fctrBusiness#Technology#` -300633327 <2e-16
## `myCategory.fctrBusiness#Crosswords/Games#` -85060197 <2e-16
## `myCategory.fctrTStyle##` -302836241 <2e-16
## `myCategory.fctrForeign#World#` -217895688 <2e-16
## `myCategory.fctrOpEd#Opinion#` 78645387 <2e-16
## `myCategory.fctrStyles##Fashion` -478330216 <2e-16
## `myCategory.fctr#Opinion#Room For Debate` -365399062 <2e-16
## `myCategory.fctr#U.S.#Education` -612930113 <2e-16
## `myCategory.fctr##` -689677812 <2e-16
## `myCategory.fctrMetro#N.Y. / Region#` -200206224 <2e-16
## `myCategory.fctrBusiness#Business Day#Small Business` -576711935 <2e-16
## `myCategory.fctrStyles#U.S.#` -125151569 <2e-16
## `myCategory.fctrTravel#Travel#` -261220073 <2e-16
## `myCategory.fctr#Opinion#The Public Editor` -3686245 <2e-16
## H.npnct03.log NA NA
## S.state.log 29881973 <2e-16
## A.state.log -28665514 <2e-16
## S.one.log 8389437 <2e-16
## A.one.log -13324617 <2e-16
## A.said.log 134488678 <2e-16
## S.said.log NA NA
## A.npnct17.log -20850361 <2e-16
## S.npnct17.log NA NA
## S.npnct08.log 35905800 <2e-16
## A.npnct08.log NA NA
## S.npnct09.log -58540795 <2e-16
## A.npnct09.log NA NA
## A.npnct27.log NA NA
## A.npnct11.log NA NA
## H.npnct11.log -54065654 <2e-16
## H.npnct22.log 18531867 <2e-16
## S.npnct02.log 4852837 <2e-16
## S.npnct11.log NA NA
## PubDate.last100.log 16515164 <2e-16
## H.npnct05.log -71152083 <2e-16
## `PubDate.date.fctr(7,13]` -28492836 <2e-16
## `PubDate.date.fctr(13,19]` 25018188 <2e-16
## `PubDate.date.fctr(19,25]` 18470112 <2e-16
## `PubDate.date.fctr(25,31]` 18804173 <2e-16
## `PubDate.second.fctr(14.8,29.5]` 29834575 <2e-16
## `PubDate.second.fctr(29.5,44.2]` 14017895 <2e-16
## `PubDate.second.fctr(44.2,59.1]` 5890120 <2e-16
## H.npnct07.log 12907912 <2e-16
## A.npnct07.log 11947483 <2e-16
## S.npnct07.log NA NA
## S.npnct03.log -8731572 <2e-16
## A.npnct19.log 24290681 <2e-16
## H.npnct13.log -10260728 <2e-16
## A.has.http NA NA
## A.npnct03.log NA NA
## A.npnct02.log NA NA
## A.npnct18.log 64403060 <2e-16
## A.npnct20.log NA NA
## A.has.year.colon 89394421 <2e-16
## S.has.year.colon NA NA
## A.npnct22.log -9795517 <2e-16
## S.npnct22.log NA NA
## H.npnct02.log -130033589 <2e-16
## A.presid.log 803742 <2e-16
## S.presid.log NA NA
## S.npnct15.log 45810321 <2e-16
## A.npnct06.log -22544507 <2e-16
## S.npnct06.log NA NA
## A.npnct15.log -45921688 <2e-16
## H.npnct14.log -33681957 <2e-16
## S.take.log -29106066 <2e-16
## A.take.log NA NA
## `PubDate.minute.fctr(14.8,29.5]` 2150583 <2e-16
## `PubDate.minute.fctr(29.5,44.2]` -23287887 <2e-16
## `PubDate.minute.fctr(44.2,59.1]` 31919969 <2e-16
## S.new.log -55694369 <2e-16
## A.new.log 55003086 <2e-16
## S.npnct13.log -34146038 <2e-16
## A.npnct13.log 31434504 <2e-16
## PubDate.wkday.fctr1 -3378732 <2e-16
## PubDate.wkday.fctr2 -19471106 <2e-16
## PubDate.wkday.fctr3 -6958116 <2e-16
## PubDate.wkday.fctr4 -12107565 <2e-16
## PubDate.wkday.fctr5 -17272384 <2e-16
## PubDate.wkday.fctr6 -60465358 <2e-16
## S.npnct30.log -38652180 <2e-16
## A.npnct30.log 35439320 <2e-16
## S.day.log -24331225 <2e-16
## A.day.log NA NA
## H.X2014.log -57454028 <2e-16
## A.show.log -263274748 <2e-16
## S.show.log NA NA
## A.npnct14.log -7638768 <2e-16
## A.report.log -246543705 <2e-16
## S.report.log NA NA
## A.year.log -9501648 <2e-16
## S.year.log NA NA
## H.npnct04.log -132773254 <2e-16
## A.share.log -28135244 <2e-16
## S.share.log NA NA
## S.compani.log -36195664 <2e-16
## A.compani.log NA NA
## H.new.log -21525505 <2e-16
## S.npnct14.log 17782463 <2e-16
## A.first.log 7250693 <2e-16
## S.first.log NA NA
## S.time.log -77546959 <2e-16
## A.time.log NA NA
## H.newyork.log -44777517 <2e-16
## A.articl.log 142560028 <2e-16
## S.articl.log NA NA
## S.will.log 20090701 <2e-16
## A.will.log -20482852 <2e-16
## H.npnct15.log 29866331 <2e-16
## A.newyork.log -95881 <2e-16
## S.newyork.log NA NA
## H.day.log 31365096 <2e-16
## A.npnct04.log -8158016 <2e-16
## S.npnct04.log NA NA
## H.today.log -15636801 <2e-16
## H.report.log -31431852 <2e-16
## H.X2015.log -25728207 <2e-16
## S.npnct16.log 44457576 <2e-16
## A.intern.log -82937899 <2e-16
## S.intern.log NA NA
## A.npnct16.log NA NA
## H.daili.log -133090276 <2e-16
## H.week.log 20438732 <2e-16
## H.has.year.colon -114343989 <2e-16
## H.fashion.log -4695702 <2e-16
## H.npnct16.log -33082262 <2e-16
## A.fashion.log -103171231 <2e-16
## S.fashion.log NA NA
## A.week.log -51020487 <2e-16
## S.week.log NA NA
## H.npnct30.log 34122519 <2e-16
## S.npnct12.log 16014464 <2e-16
## A.npnct12.log -16058289 <2e-16
## H.ndgts.log 70936178 <2e-16
## S.ndgts.log 20793245 <2e-16
## A.ndgts.log -25149154 <2e-16
## H.nuppr.log 55103374 <2e-16
## H.nchrs.log -20030894 <2e-16
## H.nwrds.log 2224121 <2e-16
## H.nwrds.unq.log -12960602 <2e-16
## A.nchrs.log 29156576 <2e-16
## S.nchrs.log -29838470 <2e-16
## A.nwrds.log -3637314 <2e-16
## S.nwrds.log 5345563 <2e-16
## A.nwrds.unq.log -21591339 <2e-16
## S.nwrds.unq.log 19678444 <2e-16
## S.nuppr.log -4718319 <2e-16
## A.nuppr.log 2773154 <2e-16
##
## (Intercept) ***
## WordCount.log ***
## `PubDate.hour.fctr(7.67,15.3]` ***
## `PubDate.hour.fctr(15.3,23]` ***
## H.npnct21.log ***
## PubDate.wkend ***
## S.npnct21.log ***
## A.npnct21.log ***
## H.npnct08.log ***
## H.npnct09.log
## PubDate.last10.log ***
## PubDate.last1.log ***
## H.npnct06.log ***
## A.can.log ***
## A.npnct01.log ***
## S.npnct01.log
## S.can.log ***
## H.npnct17.log ***
## S.npnct23.log
## S.npnct25.log
## H.has.ebola ***
## A.make.log ***
## S.make.log
## H.npnct01.log ***
## .rnorm ***
## A.npnct23.log ***
## A.npnct25.log
## H.npnct12.log ***
## `myCategory.fctrForeign#World#Asia Pacific` ***
## `myCategory.fctr#Multimedia#` ***
## `myCategory.fctrCulture#Arts#` ***
## `myCategory.fctrBusiness#Business Day#Dealbook` ***
## myCategory.fctrmyOther ***
## `myCategory.fctrBusiness#Technology#` ***
## `myCategory.fctrBusiness#Crosswords/Games#` ***
## `myCategory.fctrTStyle##` ***
## `myCategory.fctrForeign#World#` ***
## `myCategory.fctrOpEd#Opinion#` ***
## `myCategory.fctrStyles##Fashion` ***
## `myCategory.fctr#Opinion#Room For Debate` ***
## `myCategory.fctr#U.S.#Education` ***
## `myCategory.fctr##` ***
## `myCategory.fctrMetro#N.Y. / Region#` ***
## `myCategory.fctrBusiness#Business Day#Small Business` ***
## `myCategory.fctrStyles#U.S.#` ***
## `myCategory.fctrTravel#Travel#` ***
## `myCategory.fctr#Opinion#The Public Editor` ***
## H.npnct03.log
## S.state.log ***
## A.state.log ***
## S.one.log ***
## A.one.log ***
## A.said.log ***
## S.said.log
## A.npnct17.log ***
## S.npnct17.log
## S.npnct08.log ***
## A.npnct08.log
## S.npnct09.log ***
## A.npnct09.log
## A.npnct27.log
## A.npnct11.log
## H.npnct11.log ***
## H.npnct22.log ***
## S.npnct02.log ***
## S.npnct11.log
## PubDate.last100.log ***
## H.npnct05.log ***
## `PubDate.date.fctr(7,13]` ***
## `PubDate.date.fctr(13,19]` ***
## `PubDate.date.fctr(19,25]` ***
## `PubDate.date.fctr(25,31]` ***
## `PubDate.second.fctr(14.8,29.5]` ***
## `PubDate.second.fctr(29.5,44.2]` ***
## `PubDate.second.fctr(44.2,59.1]` ***
## H.npnct07.log ***
## A.npnct07.log ***
## S.npnct07.log
## S.npnct03.log ***
## A.npnct19.log ***
## H.npnct13.log ***
## A.has.http
## A.npnct03.log
## A.npnct02.log
## A.npnct18.log ***
## A.npnct20.log
## A.has.year.colon ***
## S.has.year.colon
## A.npnct22.log ***
## S.npnct22.log
## H.npnct02.log ***
## A.presid.log ***
## S.presid.log
## S.npnct15.log ***
## A.npnct06.log ***
## S.npnct06.log
## A.npnct15.log ***
## H.npnct14.log ***
## S.take.log ***
## A.take.log
## `PubDate.minute.fctr(14.8,29.5]` ***
## `PubDate.minute.fctr(29.5,44.2]` ***
## `PubDate.minute.fctr(44.2,59.1]` ***
## S.new.log ***
## A.new.log ***
## S.npnct13.log ***
## A.npnct13.log ***
## PubDate.wkday.fctr1 ***
## PubDate.wkday.fctr2 ***
## PubDate.wkday.fctr3 ***
## PubDate.wkday.fctr4 ***
## PubDate.wkday.fctr5 ***
## PubDate.wkday.fctr6 ***
## S.npnct30.log ***
## A.npnct30.log ***
## S.day.log ***
## A.day.log
## H.X2014.log ***
## A.show.log ***
## S.show.log
## A.npnct14.log ***
## A.report.log ***
## S.report.log
## A.year.log ***
## S.year.log
## H.npnct04.log ***
## A.share.log ***
## S.share.log
## S.compani.log ***
## A.compani.log
## H.new.log ***
## S.npnct14.log ***
## A.first.log ***
## S.first.log
## S.time.log ***
## A.time.log
## H.newyork.log ***
## A.articl.log ***
## S.articl.log
## S.will.log ***
## A.will.log ***
## H.npnct15.log ***
## A.newyork.log ***
## S.newyork.log
## H.day.log ***
## A.npnct04.log ***
## S.npnct04.log
## H.today.log ***
## H.report.log ***
## H.X2015.log ***
## S.npnct16.log ***
## A.intern.log ***
## S.intern.log
## A.npnct16.log
## H.daili.log ***
## H.week.log ***
## H.has.year.colon ***
## H.fashion.log ***
## H.npnct16.log ***
## A.fashion.log ***
## S.fashion.log
## A.week.log ***
## S.week.log
## H.npnct30.log ***
## S.npnct12.log ***
## A.npnct12.log ***
## H.ndgts.log ***
## S.ndgts.log ***
## A.ndgts.log ***
## H.nuppr.log ***
## H.nchrs.log ***
## H.nwrds.log ***
## H.nwrds.unq.log ***
## A.nchrs.log ***
## S.nchrs.log ***
## A.nwrds.log ***
## S.nwrds.log ***
## A.nwrds.unq.log ***
## S.nwrds.unq.log ***
## S.nuppr.log ***
## A.nuppr.log ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 33664.8 on 4333 degrees of freedom
## AIC: 33949
##
## Number of Fisher Scoring iterations: 25
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.6965562
## 3 0.2 0.6965562
## 4 0.3 0.6965562
## 5 0.4 0.6965562
## 6 0.5 0.6965562
## 7 0.6 0.6965562
## 8 0.7 0.6965562
## 9 0.8 0.6965562
## 10 0.9 0.6965562
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.All.X.glm.N
## 1 N 3472
## 2 Y 213
## Popular.fctr.predict.All.X.glm.Y
## 1 254
## 2 536
## Prediction
## Reference N Y
## N 3472 254
## Y 213 536
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.956425e-01 6.335958e-01 8.863137e-01 9.044512e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 3.044288e-33 6.417254e-02
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.6892655
## 3 0.2 0.6892655
## 4 0.3 0.6892655
## 5 0.4 0.6892655
## 6 0.5 0.6892655
## 7 0.6 0.6892655
## 8 0.7 0.6892655
## 9 0.8 0.6892655
## 10 0.9 0.6892655
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.All.X.glm.N
## 1 N 1593
## 2 Y 100
## Popular.fctr.predict.All.X.glm.Y
## 1 120
## 2 244
## Prediction
## Reference N Y
## N 1593 120
## Y 100 244
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.930481e-01 6.247359e-01 8.788850e-01 9.060795e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 6.262798e-15 2.002008e-01
## model_id model_method
## 1 All.X.glm glm
## feats
## 1 WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, S.npnct21.log, A.npnct21.log, H.npnct08.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, H.npnct06.log, A.can.log, A.npnct01.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct23.log, S.npnct25.log, H.has.ebola, A.make.log, S.make.log, H.npnct01.log, .rnorm, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, A.npnct22.log, S.npnct22.log, H.npnct02.log, A.presid.log, S.presid.log, S.npnct15.log, A.npnct06.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, A.take.log, PubDate.minute.fctr, S.new.log, A.new.log, S.npnct13.log, A.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, A.npnct30.log, S.day.log, A.day.log, H.X2014.log, A.show.log, S.show.log, A.npnct14.log, A.report.log, S.report.log, A.year.log, S.year.log, H.npnct04.log, A.share.log, S.share.log, S.compani.log, A.compani.log, H.new.log, S.npnct14.log, A.first.log, S.first.log, S.time.log, A.time.log, H.newyork.log, A.articl.log, S.articl.log, S.will.log, A.will.log, H.npnct15.log, A.newyork.log, S.newyork.log, H.day.log, A.npnct04.log, S.npnct04.log, H.today.log, H.report.log, H.X2015.log, S.npnct16.log, A.intern.log, S.intern.log, A.npnct16.log, H.daili.log, H.week.log, H.has.year.colon, H.fashion.log, H.npnct16.log, A.fashion.log, S.fashion.log, A.week.log, S.week.log, H.npnct30.log, S.npnct12.log, A.npnct12.log, H.ndgts.log, S.ndgts.log, A.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, H.nwrds.unq.log, A.nchrs.log, S.nchrs.log, A.nwrds.log, S.nwrds.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, A.nuppr.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 23.501 7.287
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8237256 0.9 0.6965562 0.8634728
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8863137 0.9044512 0.5545249 0.8196249
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.9 0.6892655 0.8930481
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.878885 0.9060795 0.6247359 33948.77
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.07748035 0.1722791
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, S.npnct21.log, A.npnct21.log, H.npnct08.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, H.npnct06.log, A.can.log, A.npnct01.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct23.log, S.npnct25.log, H.has.ebola, A.make.log, S.make.log, H.npnct01.log, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, A.npnct22.log, S.npnct22.log, H.npnct02.log, A.presid.log, S.presid.log, S.npnct15.log, A.npnct06.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, A.take.log, PubDate.minute.fctr, S.new.log, A.new.log, S.npnct13.log, A.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, A.npnct30.log, S.day.log, A.day.log, H.X2014.log, A.show.log, S.show.log, A.npnct14.log, A.report.log, S.report.log, A.year.log, S.year.log, H.npnct04.log, A.share.log, S.share.log, S.compani.log, A.compani.log, H.new.log, S.npnct14.log, A.first.log, S.first.log, S.time.log, A.time.log, H.newyork.log, A.articl.log, S.articl.log, S.will.log, A.will.log, H.npnct15.log, A.newyork.log, S.newyork.log, H.day.log, A.npnct04.log, S.npnct04.log, H.today.log, H.report.log, H.X2015.log, S.npnct16.log, A.intern.log, S.intern.log, A.npnct16.log, H.daili.log, H.week.log, H.has.year.colon, H.fashion.log, H.npnct16.log, A.fashion.log, S.fashion.log, A.week.log, S.week.log, H.npnct30.log, S.npnct12.log, A.npnct12.log, H.ndgts.log, S.ndgts.log, A.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, H.nwrds.unq.log, A.nchrs.log, S.nchrs.log, A.nwrds.log, S.nwrds.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, A.nuppr.log"
## + Fold1: cp=0.01135
## - Fold1: cp=0.01135
## + Fold2: cp=0.01135
## - Fold2: cp=0.01135
## + Fold3: cp=0.01135
## - Fold3: cp=0.01135
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0113 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4475
##
## CP nsplit rel error
## 1 0.27102804 0 1.0000000
## 2 0.08411215 1 0.7289720
## 3 0.01134846 2 0.6448598
##
## Variable importance
## myCategory.fctrOpEd#Opinion#
## 51
## myCategory.fctrBusiness#Crosswords/Games#
## 17
## A.nwrds.unq.log
## 6
## A.nwrds.log
## 6
## S.nwrds.unq.log
## 6
## S.nwrds.log
## 6
## A.nchrs.log
## 6
## H.nchrs.log
## 1
##
## Node number 1: 4475 observations, complexity param=0.271028
## predicted class=N expected loss=0.1673743 P(node) =1
## class counts: 3726 749
## probabilities: 0.833 0.167
## left son=2 (4106 obs) right son=3 (369 obs)
## Primary splits:
## myCategory.fctrOpEd#Opinion# < 0.5 to the left, improve=297.02950, (0 missing)
## WordCount.log < 6.524296 to the left, improve=104.85530, (0 missing)
## S.nuppr.log < 1.497866 to the right, improve= 86.35796, (0 missing)
## A.nuppr.log < 1.497866 to the right, improve= 86.35796, (0 missing)
## myCategory.fctrBusiness#Crosswords/Games# < 0.5 to the left, improve= 85.77765, (0 missing)
## Surrogate splits:
## A.nwrds.unq.log < 1.497866 to the right, agree=0.928, adj=0.127, (0 split)
## A.nwrds.log < 1.497866 to the right, agree=0.928, adj=0.125, (0 split)
## S.nwrds.unq.log < 1.497866 to the right, agree=0.928, adj=0.125, (0 split)
## S.nwrds.log < 1.497866 to the right, agree=0.928, adj=0.122, (0 split)
## A.nchrs.log < 3.725621 to the right, agree=0.927, adj=0.117, (0 split)
##
## Node number 2: 4106 observations, complexity param=0.08411215
## predicted class=N expected loss=0.1127618 P(node) =0.9175419
## class counts: 3643 463
## probabilities: 0.887 0.113
## left son=4 (4023 obs) right son=5 (83 obs)
## Primary splits:
## myCategory.fctrBusiness#Crosswords/Games# < 0.5 to the left, improve=99.60741, (0 missing)
## WordCount.log < 6.470025 to the left, improve=94.06998, (0 missing)
## myCategory.fctrStyles#U.S.# < 0.5 to the left, improve=50.94648, (0 missing)
## S.nuppr.log < 1.497866 to the right, improve=31.44556, (0 missing)
## A.nuppr.log < 1.497866 to the right, improve=31.44556, (0 missing)
## Surrogate splits:
## H.nchrs.log < 2.35024 to the right, agree=0.981, adj=0.060, (0 split)
## H.nuppr.log < 0.8958797 to the right, agree=0.980, adj=0.024, (0 split)
##
## Node number 3: 369 observations
## predicted class=Y expected loss=0.2249322 P(node) =0.0824581
## class counts: 83 286
## probabilities: 0.225 0.775
##
## Node number 4: 4023 observations
## predicted class=N expected loss=0.09694258 P(node) =0.8989944
## class counts: 3633 390
## probabilities: 0.903 0.097
##
## Node number 5: 83 observations
## predicted class=Y expected loss=0.1204819 P(node) =0.01854749
## class counts: 10 73
## probabilities: 0.120 0.880
##
## n= 4475
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4475 749 N (0.83262570 0.16737430)
## 2) myCategory.fctrOpEd#Opinion#< 0.5 4106 463 N (0.88723819 0.11276181)
## 4) myCategory.fctrBusiness#Crosswords/Games#< 0.5 4023 390 N (0.90305742 0.09694258) *
## 5) myCategory.fctrBusiness#Crosswords/Games#>=0.5 83 10 Y (0.12048193 0.87951807) *
## 3) myCategory.fctrOpEd#Opinion#>=0.5 369 83 Y (0.22493225 0.77506775) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.5978351
## 3 0.2 0.5978351
## 4 0.3 0.5978351
## 5 0.4 0.5978351
## 6 0.5 0.5978351
## 7 0.6 0.5978351
## 8 0.7 0.5978351
## 9 0.8 0.1754808
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 3633
## 2 Y 390
## Popular.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 93
## 2 359
## Prediction
## Reference N Y
## N 3633 93
## Y 390 359
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.920670e-01 5.398657e-01 8.826068e-01 9.010121e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 1.439953e-29 2.397951e-41
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.5650558
## 3 0.2 0.5650558
## 4 0.3 0.5650558
## 5 0.4 0.5650558
## 6 0.5 0.5650558
## 7 0.6 0.5650558
## 8 0.7 0.5650558
## 9 0.8 0.1562500
## 10 0.9 0.0000000
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 1671
## 2 Y 192
## Popular.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 42
## 2 152
## Prediction
## Reference N Y
## N 1671 42
## Y 192 152
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.862421e-01 5.054039e-01 8.717239e-01 8.996488e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 5.783557e-12 2.026854e-22
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, S.npnct21.log, A.npnct21.log, H.npnct08.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, H.npnct06.log, A.can.log, A.npnct01.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct23.log, S.npnct25.log, H.has.ebola, A.make.log, S.make.log, H.npnct01.log, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, A.npnct22.log, S.npnct22.log, H.npnct02.log, A.presid.log, S.presid.log, S.npnct15.log, A.npnct06.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, A.take.log, PubDate.minute.fctr, S.new.log, A.new.log, S.npnct13.log, A.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, A.npnct30.log, S.day.log, A.day.log, H.X2014.log, A.show.log, S.show.log, A.npnct14.log, A.report.log, S.report.log, A.year.log, S.year.log, H.npnct04.log, A.share.log, S.share.log, S.compani.log, A.compani.log, H.new.log, S.npnct14.log, A.first.log, S.first.log, S.time.log, A.time.log, H.newyork.log, A.articl.log, S.articl.log, S.will.log, A.will.log, H.npnct15.log, A.newyork.log, S.newyork.log, H.day.log, A.npnct04.log, S.npnct04.log, H.today.log, H.report.log, H.X2015.log, S.npnct16.log, A.intern.log, S.intern.log, A.npnct16.log, H.daili.log, H.week.log, H.has.year.colon, H.fashion.log, H.npnct16.log, A.fashion.log, S.fashion.log, A.week.log, S.week.log, H.npnct30.log, S.npnct12.log, A.npnct12.log, H.ndgts.log, S.ndgts.log, A.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, H.nwrds.unq.log, A.nchrs.log, S.nchrs.log, A.nwrds.log, S.nwrds.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, A.nuppr.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 10.65 1.924
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7277461 0.7 0.5978351 0.8934084
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8826068 0.9010121 0.5566659 0.7084504
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.5650558 0.8862421
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8717239 0.8996488 0.5054039
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.003041136 0.02922293
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, S.npnct21.log, A.npnct21.log, H.npnct08.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, H.npnct06.log, A.can.log, A.npnct01.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct23.log, S.npnct25.log, H.has.ebola, A.make.log, S.make.log, H.npnct01.log, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, A.npnct22.log, S.npnct22.log, H.npnct02.log, A.presid.log, S.presid.log, S.npnct15.log, A.npnct06.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, A.take.log, PubDate.minute.fctr, S.new.log, A.new.log, S.npnct13.log, A.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, A.npnct30.log, S.day.log, A.day.log, H.X2014.log, A.show.log, S.show.log, A.npnct14.log, A.report.log, S.report.log, A.year.log, S.year.log, H.npnct04.log, A.share.log, S.share.log, S.compani.log, A.compani.log, H.new.log, S.npnct14.log, A.first.log, S.first.log, S.time.log, A.time.log, H.newyork.log, A.articl.log, S.articl.log, S.will.log, A.will.log, H.npnct15.log, A.newyork.log, S.newyork.log, H.day.log, A.npnct04.log, S.npnct04.log, H.today.log, H.report.log, H.X2015.log, S.npnct16.log, A.intern.log, S.intern.log, A.npnct16.log, H.daili.log, H.week.log, H.has.year.colon, H.fashion.log, H.npnct16.log, A.fashion.log, S.fashion.log, A.week.log, S.week.log, H.npnct30.log, S.npnct12.log, A.npnct12.log, H.ndgts.log, S.ndgts.log, A.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, H.nwrds.unq.log, A.nchrs.log, S.nchrs.log, A.nwrds.log, S.nwrds.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, A.nuppr.log"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
## + : mtry= 2
## - : mtry= 2
## + : mtry= 90
## - : mtry= 90
## + : mtry=179
## - : mtry=179
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 90 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 4475 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 8950 matrix numeric
## oob.times 4475 -none- numeric
## classes 2 -none- character
## importance 179 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 4475 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 179 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.28675345
## 2 0.1 0.78759201
## 3 0.2 0.93159204
## 4 0.3 0.98423127
## 5 0.4 1.00000000
## 6 0.5 1.00000000
## 7 0.6 0.99866310
## 8 0.7 0.92230216
## 9 0.8 0.78507705
## 10 0.9 0.55544841
## 11 1.0 0.01062417
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 3726
## 2 Y NA
## Popular.fctr.predict.All.X.no.rnorm.rf.Y
## 1 NA
## 2 749
## Prediction
## Reference N Y
## N 3726 0
## Y 0 749
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.0000000 1.0000000 0.9991760 1.0000000 0.8326257
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.5567190
## 3 0.2 0.6438356
## 4 0.3 0.6810229
## 5 0.4 0.6725146
## 6 0.5 0.6612111
## 7 0.6 0.6276596
## 8 0.7 0.5581395
## 9 0.8 0.4478261
## 10 0.9 0.2436548
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 1567
## 2 Y 91
## Popular.fctr.predict.All.X.no.rnorm.rf.Y
## 1 146
## 2 253
## Prediction
## Reference N Y
## N 1567 146
## Y 91 253
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.847837e-01 6.111869e-01 8.701915e-01 8.982687e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 2.209845e-11 4.520376e-04
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, S.npnct21.log, A.npnct21.log, H.npnct08.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, H.npnct06.log, A.can.log, A.npnct01.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct23.log, S.npnct25.log, H.has.ebola, A.make.log, S.make.log, H.npnct01.log, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, A.npnct22.log, S.npnct22.log, H.npnct02.log, A.presid.log, S.presid.log, S.npnct15.log, A.npnct06.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, A.take.log, PubDate.minute.fctr, S.new.log, A.new.log, S.npnct13.log, A.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, A.npnct30.log, S.day.log, A.day.log, H.X2014.log, A.show.log, S.show.log, A.npnct14.log, A.report.log, S.report.log, A.year.log, S.year.log, H.npnct04.log, A.share.log, S.share.log, S.compani.log, A.compani.log, H.new.log, S.npnct14.log, A.first.log, S.first.log, S.time.log, A.time.log, H.newyork.log, A.articl.log, S.articl.log, S.will.log, A.will.log, H.npnct15.log, A.newyork.log, S.newyork.log, H.day.log, A.npnct04.log, S.npnct04.log, H.today.log, H.report.log, H.X2015.log, S.npnct16.log, A.intern.log, S.intern.log, A.npnct16.log, H.daili.log, H.week.log, H.has.year.colon, H.fashion.log, H.npnct16.log, A.fashion.log, S.fashion.log, A.week.log, S.week.log, H.npnct30.log, S.npnct12.log, A.npnct12.log, H.ndgts.log, S.ndgts.log, A.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, H.nwrds.unq.log, A.nchrs.log, S.nchrs.log, A.nwrds.log, S.nwrds.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, A.nuppr.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 269.212 67.345
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 1 0.5 1 0.9054749
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.999176 1 0.629002 0.9189958
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.6810229 0.8847837
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8701915 0.8982687 0.6111869# User specified
# easier to exclude features
#model_id_pfx <- "";
# indep_vars_vctr <- setdiff(names(glb_fitent_df),
# union(union(glb_rsp_var, glb_exclude_vars_as_features),
# c("<feat1_name>", "<feat2_name>")))
# method <- ""
# easier to include features
#model_id_pfx <- ""; indep_vars_vctr <- c("<feat1_name>", "<feat1_name>"); method <- ""
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitent_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitent_df, OOB_df=glb_OOBent_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitent_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitent_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## 1 MFO.myMFO_classfr myMFO_classfr
## 2 Random.myrandom_classfr myrandom_classfr
## 3 Max.cor.Y.cv.0.rpart rpart
## 4 Max.cor.Y.cv.0.cp.0.rpart rpart
## 5 Max.cor.Y.rpart rpart
## 6 Max.cor.Y.glm glm
## 7 Interact.High.cor.Y.glm glm
## 8 Low.cor.X.glm glm
## 9 All.X.glm glm
## 10 All.X.no.rnorm.rpart rpart
## 11 All.X.no.rnorm.rf rf
## feats
## 1 .rnorm
## 2 .rnorm
## 3 A.nuppr.log
## 4 A.nuppr.log
## 5 A.nuppr.log
## 6 A.nuppr.log
## 7 A.nuppr.log, A.nuppr.log:A.npnct21.log, A.nuppr.log:H.npnct09.log, A.nuppr.log:H.npnct17.log, A.nuppr.log:S.can.log, A.nuppr.log:S.npnct01.log, A.nuppr.log:S.npnct25.log, A.nuppr.log:S.make.log, A.nuppr.log:S.npnct22.log, A.nuppr.log:S.presid.log, A.nuppr.log:S.npnct06.log, A.nuppr.log:S.take.log, A.nuppr.log:S.new.log, A.nuppr.log:S.npnct13.log, A.nuppr.log:S.npnct30.log, A.nuppr.log:S.day.log, A.nuppr.log:S.show.log, A.nuppr.log:S.report.log, A.nuppr.log:S.year.log, A.nuppr.log:S.share.log, A.nuppr.log:S.compani.log, A.nuppr.log:A.npnct14.log, A.nuppr.log:S.first.log, A.nuppr.log:S.time.log, A.nuppr.log:S.articl.log, A.nuppr.log:S.will.log, A.nuppr.log:S.newyork.log, A.nuppr.log:S.npnct04.log, A.nuppr.log:H.npnct15.log, A.nuppr.log:S.intern.log, A.nuppr.log:S.npnct16.log, A.nuppr.log:A.intern.log, A.nuppr.log:H.week.log, A.nuppr.log:S.fashion.log, A.nuppr.log:S.week.log, A.nuppr.log:S.npnct12.log, A.nuppr.log:S.ndgts.log, A.nuppr.log:H.nuppr.log, A.nuppr.log:A.nchrs.log, A.nuppr.log:A.nwrds.log, A.nuppr.log:S.nchrs.log, A.nuppr.log:S.nuppr.log
## 8 WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, A.npnct21.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct25.log, H.has.ebola, S.make.log, H.npnct01.log, .rnorm, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, S.npnct22.log, H.npnct02.log, S.presid.log, S.npnct15.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, PubDate.minute.fctr, S.new.log, S.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, S.day.log, H.X2014.log, S.show.log, A.npnct14.log, S.report.log, S.year.log, H.npnct04.log, S.share.log, S.compani.log, H.new.log, S.first.log, S.time.log, H.newyork.log, S.articl.log, S.will.log, H.npnct15.log, S.newyork.log, H.day.log, S.npnct04.log, H.today.log, H.report.log, S.npnct16.log, S.intern.log, H.daili.log, H.week.log, H.npnct16.log, S.fashion.log, S.week.log, H.npnct30.log, S.npnct12.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, A.nchrs.log, A.nwrds.log, A.nwrds.unq.log, S.nuppr.log
## 9 WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, S.npnct21.log, A.npnct21.log, H.npnct08.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, H.npnct06.log, A.can.log, A.npnct01.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct23.log, S.npnct25.log, H.has.ebola, A.make.log, S.make.log, H.npnct01.log, .rnorm, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, A.npnct22.log, S.npnct22.log, H.npnct02.log, A.presid.log, S.presid.log, S.npnct15.log, A.npnct06.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, A.take.log, PubDate.minute.fctr, S.new.log, A.new.log, S.npnct13.log, A.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, A.npnct30.log, S.day.log, A.day.log, H.X2014.log, A.show.log, S.show.log, A.npnct14.log, A.report.log, S.report.log, A.year.log, S.year.log, H.npnct04.log, A.share.log, S.share.log, S.compani.log, A.compani.log, H.new.log, S.npnct14.log, A.first.log, S.first.log, S.time.log, A.time.log, H.newyork.log, A.articl.log, S.articl.log, S.will.log, A.will.log, H.npnct15.log, A.newyork.log, S.newyork.log, H.day.log, A.npnct04.log, S.npnct04.log, H.today.log, H.report.log, H.X2015.log, S.npnct16.log, A.intern.log, S.intern.log, A.npnct16.log, H.daili.log, H.week.log, H.has.year.colon, H.fashion.log, H.npnct16.log, A.fashion.log, S.fashion.log, A.week.log, S.week.log, H.npnct30.log, S.npnct12.log, A.npnct12.log, H.ndgts.log, S.ndgts.log, A.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, H.nwrds.unq.log, A.nchrs.log, S.nchrs.log, A.nwrds.log, S.nwrds.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, A.nuppr.log
## 10 WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, S.npnct21.log, A.npnct21.log, H.npnct08.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, H.npnct06.log, A.can.log, A.npnct01.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct23.log, S.npnct25.log, H.has.ebola, A.make.log, S.make.log, H.npnct01.log, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, A.npnct22.log, S.npnct22.log, H.npnct02.log, A.presid.log, S.presid.log, S.npnct15.log, A.npnct06.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, A.take.log, PubDate.minute.fctr, S.new.log, A.new.log, S.npnct13.log, A.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, A.npnct30.log, S.day.log, A.day.log, H.X2014.log, A.show.log, S.show.log, A.npnct14.log, A.report.log, S.report.log, A.year.log, S.year.log, H.npnct04.log, A.share.log, S.share.log, S.compani.log, A.compani.log, H.new.log, S.npnct14.log, A.first.log, S.first.log, S.time.log, A.time.log, H.newyork.log, A.articl.log, S.articl.log, S.will.log, A.will.log, H.npnct15.log, A.newyork.log, S.newyork.log, H.day.log, A.npnct04.log, S.npnct04.log, H.today.log, H.report.log, H.X2015.log, S.npnct16.log, A.intern.log, S.intern.log, A.npnct16.log, H.daili.log, H.week.log, H.has.year.colon, H.fashion.log, H.npnct16.log, A.fashion.log, S.fashion.log, A.week.log, S.week.log, H.npnct30.log, S.npnct12.log, A.npnct12.log, H.ndgts.log, S.ndgts.log, A.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, H.nwrds.unq.log, A.nchrs.log, S.nchrs.log, A.nwrds.log, S.nwrds.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, A.nuppr.log
## 11 WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, S.npnct21.log, A.npnct21.log, H.npnct08.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, H.npnct06.log, A.can.log, A.npnct01.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct23.log, S.npnct25.log, H.has.ebola, A.make.log, S.make.log, H.npnct01.log, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, A.npnct22.log, S.npnct22.log, H.npnct02.log, A.presid.log, S.presid.log, S.npnct15.log, A.npnct06.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, A.take.log, PubDate.minute.fctr, S.new.log, A.new.log, S.npnct13.log, A.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, A.npnct30.log, S.day.log, A.day.log, H.X2014.log, A.show.log, S.show.log, A.npnct14.log, A.report.log, S.report.log, A.year.log, S.year.log, H.npnct04.log, A.share.log, S.share.log, S.compani.log, A.compani.log, H.new.log, S.npnct14.log, A.first.log, S.first.log, S.time.log, A.time.log, H.newyork.log, A.articl.log, S.articl.log, S.will.log, A.will.log, H.npnct15.log, A.newyork.log, S.newyork.log, H.day.log, A.npnct04.log, S.npnct04.log, H.today.log, H.report.log, H.X2015.log, S.npnct16.log, A.intern.log, S.intern.log, A.npnct16.log, H.daili.log, H.week.log, H.has.year.colon, H.fashion.log, H.npnct16.log, A.fashion.log, S.fashion.log, A.week.log, S.week.log, H.npnct30.log, S.npnct12.log, A.npnct12.log, H.ndgts.log, S.ndgts.log, A.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, H.nwrds.unq.log, A.nchrs.log, S.nchrs.log, A.nwrds.log, S.nwrds.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, A.nuppr.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.673 0.003
## 2 0 0.338 0.001
## 3 0 0.659 0.054
## 4 0 0.595 0.053
## 5 1 1.196 0.053
## 6 1 1.122 0.077
## 7 1 3.489 0.818
## 8 1 14.959 4.685
## 9 1 23.501 7.287
## 10 3 10.650 1.924
## 11 3 269.212 67.345
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5000000 0.5 0.0000000 0.8326257
## 2 0.5007516 0.1 0.2867534 0.1673743
## 3 0.5000000 0.5 0.0000000 0.8326257
## 4 0.5000000 0.5 0.0000000 0.8326257
## 5 0.5000000 0.5 0.0000000 0.8326258
## 6 0.7073742 0.2 0.3986014 0.8324022
## 7 0.7922820 0.3 0.4665885 0.8402235
## 8 0.9487497 0.3 0.7441860 0.8929586
## 9 0.8237256 0.9 0.6965562 0.8634728
## 10 0.7277461 0.7 0.5978351 0.8934084
## 11 1.0000000 0.5 1.0000000 0.9054749
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8213602 0.8434553 0.0000000000 0.5000000
## 2 0.1565447 0.1786398 0.0000000000 0.4909227
## 3 0.8213602 0.8434553 0.0000000000 0.5000000
## 4 0.8213602 0.8434553 0.0000000000 0.5000000
## 5 0.8213602 0.8434553 0.0000000000 0.5000000
## 6 0.7176970 0.7439004 -0.0004459345 0.7102060
## 7 0.7845519 0.8083558 0.0995997038 0.7766863
## 8 0.9002448 0.9173176 0.6067690411 0.9226367
## 9 0.8863137 0.9044512 0.5545248700 0.8196249
## 10 0.8826068 0.9010121 0.5566658958 0.7084504
## 11 0.9991760 1.0000000 0.6290019836 0.9189958
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.0000000 0.8327662
## 2 0.1 0.2865473 0.1672338
## 3 0.5 0.0000000 0.8327662
## 4 0.5 0.0000000 0.8327662
## 5 0.5 0.0000000 0.8327662
## 6 0.2 0.3880266 0.7316480
## 7 0.3 0.4595635 0.7953330
## 8 0.3 0.7252747 0.9027710
## 9 0.9 0.6892655 0.8930481
## 10 0.7 0.5650558 0.8862421
## 11 0.3 0.6810229 0.8847837
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8159247 0.8486533 0.0000000
## 2 0.1513467 0.1840753 0.0000000
## 3 0.8159247 0.8486533 0.0000000
## 4 0.8159247 0.8486533 0.0000000
## 5 0.8159247 0.8486533 0.0000000
## 6 0.7119353 0.7506985 0.2283681
## 7 0.7772394 0.8125808 0.3354449
## 8 0.8891448 0.9152364 0.6664245
## 9 0.8788850 0.9060795 0.6247359
## 10 0.8717239 0.8996488 0.5054039
## 11 0.8701915 0.8982687 0.6111869
## max.AccuracySD.fit max.KappaSD.fit min.aic.fit
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 0.0002791548 0.0000000000 NA
## 6 0.0000648833 0.0007723812 3714.601
## 7 0.0003600752 0.0163976516 3419.307
## 8 0.0155625583 0.0424230615 2092.942
## 9 0.0774803506 0.1722790551 33948.772
## 10 0.0030411362 0.0292229339 NA
## 11 NA NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 6 1 222.291 544.291 322
## 11 fit.models 6 2 544.291 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitent_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBent_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
# tmp_models_df <- orderBy(~model_id, glb_models_df)
# rownames(tmp_models_df) <- seq(1, nrow(tmp_models_df))
# all.equal(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr"),
# subset(stats_df, model_id != "Random.myrandom_classfr"))
# print(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
# print(subset(stats_df, model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id", grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df), grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## 1 MFO.myMFO_classfr myMFO_classfr
## 2 Random.myrandom_classfr myrandom_classfr
## 3 Max.cor.Y.cv.0.rpart rpart
## 4 Max.cor.Y.cv.0.cp.0.rpart rpart
## 5 Max.cor.Y.rpart rpart
## 6 Max.cor.Y.glm glm
## 7 Interact.High.cor.Y.glm glm
## 8 Low.cor.X.glm glm
## 9 All.X.glm glm
## 10 All.X.no.rnorm.rpart rpart
## 11 All.X.no.rnorm.rf rf
## feats
## 1 .rnorm
## 2 .rnorm
## 3 A.nuppr.log
## 4 A.nuppr.log
## 5 A.nuppr.log
## 6 A.nuppr.log
## 7 A.nuppr.log, A.nuppr.log:A.npnct21.log, A.nuppr.log:H.npnct09.log, A.nuppr.log:H.npnct17.log, A.nuppr.log:S.can.log, A.nuppr.log:S.npnct01.log, A.nuppr.log:S.npnct25.log, A.nuppr.log:S.make.log, A.nuppr.log:S.npnct22.log, A.nuppr.log:S.presid.log, A.nuppr.log:S.npnct06.log, A.nuppr.log:S.take.log, A.nuppr.log:S.new.log, A.nuppr.log:S.npnct13.log, A.nuppr.log:S.npnct30.log, A.nuppr.log:S.day.log, A.nuppr.log:S.show.log, A.nuppr.log:S.report.log, A.nuppr.log:S.year.log, A.nuppr.log:S.share.log, A.nuppr.log:S.compani.log, A.nuppr.log:A.npnct14.log, A.nuppr.log:S.first.log, A.nuppr.log:S.time.log, A.nuppr.log:S.articl.log, A.nuppr.log:S.will.log, A.nuppr.log:S.newyork.log, A.nuppr.log:S.npnct04.log, A.nuppr.log:H.npnct15.log, A.nuppr.log:S.intern.log, A.nuppr.log:S.npnct16.log, A.nuppr.log:A.intern.log, A.nuppr.log:H.week.log, A.nuppr.log:S.fashion.log, A.nuppr.log:S.week.log, A.nuppr.log:S.npnct12.log, A.nuppr.log:S.ndgts.log, A.nuppr.log:H.nuppr.log, A.nuppr.log:A.nchrs.log, A.nuppr.log:A.nwrds.log, A.nuppr.log:S.nchrs.log, A.nuppr.log:S.nuppr.log
## 8 WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, A.npnct21.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct25.log, H.has.ebola, S.make.log, H.npnct01.log, .rnorm, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, S.npnct22.log, H.npnct02.log, S.presid.log, S.npnct15.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, PubDate.minute.fctr, S.new.log, S.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, S.day.log, H.X2014.log, S.show.log, A.npnct14.log, S.report.log, S.year.log, H.npnct04.log, S.share.log, S.compani.log, H.new.log, S.first.log, S.time.log, H.newyork.log, S.articl.log, S.will.log, H.npnct15.log, S.newyork.log, H.day.log, S.npnct04.log, H.today.log, H.report.log, S.npnct16.log, S.intern.log, H.daili.log, H.week.log, H.npnct16.log, S.fashion.log, S.week.log, H.npnct30.log, S.npnct12.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, A.nchrs.log, A.nwrds.log, A.nwrds.unq.log, S.nuppr.log
## 9 WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, S.npnct21.log, A.npnct21.log, H.npnct08.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, H.npnct06.log, A.can.log, A.npnct01.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct23.log, S.npnct25.log, H.has.ebola, A.make.log, S.make.log, H.npnct01.log, .rnorm, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, A.npnct22.log, S.npnct22.log, H.npnct02.log, A.presid.log, S.presid.log, S.npnct15.log, A.npnct06.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, A.take.log, PubDate.minute.fctr, S.new.log, A.new.log, S.npnct13.log, A.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, A.npnct30.log, S.day.log, A.day.log, H.X2014.log, A.show.log, S.show.log, A.npnct14.log, A.report.log, S.report.log, A.year.log, S.year.log, H.npnct04.log, A.share.log, S.share.log, S.compani.log, A.compani.log, H.new.log, S.npnct14.log, A.first.log, S.first.log, S.time.log, A.time.log, H.newyork.log, A.articl.log, S.articl.log, S.will.log, A.will.log, H.npnct15.log, A.newyork.log, S.newyork.log, H.day.log, A.npnct04.log, S.npnct04.log, H.today.log, H.report.log, H.X2015.log, S.npnct16.log, A.intern.log, S.intern.log, A.npnct16.log, H.daili.log, H.week.log, H.has.year.colon, H.fashion.log, H.npnct16.log, A.fashion.log, S.fashion.log, A.week.log, S.week.log, H.npnct30.log, S.npnct12.log, A.npnct12.log, H.ndgts.log, S.ndgts.log, A.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, H.nwrds.unq.log, A.nchrs.log, S.nchrs.log, A.nwrds.log, S.nwrds.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, A.nuppr.log
## 10 WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, S.npnct21.log, A.npnct21.log, H.npnct08.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, H.npnct06.log, A.can.log, A.npnct01.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct23.log, S.npnct25.log, H.has.ebola, A.make.log, S.make.log, H.npnct01.log, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, A.npnct22.log, S.npnct22.log, H.npnct02.log, A.presid.log, S.presid.log, S.npnct15.log, A.npnct06.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, A.take.log, PubDate.minute.fctr, S.new.log, A.new.log, S.npnct13.log, A.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, A.npnct30.log, S.day.log, A.day.log, H.X2014.log, A.show.log, S.show.log, A.npnct14.log, A.report.log, S.report.log, A.year.log, S.year.log, H.npnct04.log, A.share.log, S.share.log, S.compani.log, A.compani.log, H.new.log, S.npnct14.log, A.first.log, S.first.log, S.time.log, A.time.log, H.newyork.log, A.articl.log, S.articl.log, S.will.log, A.will.log, H.npnct15.log, A.newyork.log, S.newyork.log, H.day.log, A.npnct04.log, S.npnct04.log, H.today.log, H.report.log, H.X2015.log, S.npnct16.log, A.intern.log, S.intern.log, A.npnct16.log, H.daili.log, H.week.log, H.has.year.colon, H.fashion.log, H.npnct16.log, A.fashion.log, S.fashion.log, A.week.log, S.week.log, H.npnct30.log, S.npnct12.log, A.npnct12.log, H.ndgts.log, S.ndgts.log, A.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, H.nwrds.unq.log, A.nchrs.log, S.nchrs.log, A.nwrds.log, S.nwrds.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, A.nuppr.log
## 11 WordCount.log, PubDate.hour.fctr, H.npnct21.log, PubDate.wkend, S.npnct21.log, A.npnct21.log, H.npnct08.log, H.npnct09.log, PubDate.last10.log, PubDate.last1.log, H.npnct06.log, A.can.log, A.npnct01.log, S.npnct01.log, S.can.log, H.npnct17.log, S.npnct23.log, S.npnct25.log, H.has.ebola, A.make.log, S.make.log, H.npnct01.log, A.npnct23.log, A.npnct25.log, H.npnct12.log, myCategory.fctr, H.npnct03.log, S.state.log, A.state.log, S.one.log, A.one.log, A.said.log, S.said.log, A.npnct17.log, S.npnct17.log, S.npnct08.log, A.npnct08.log, S.npnct09.log, A.npnct09.log, A.npnct27.log, A.npnct11.log, H.npnct11.log, H.npnct22.log, S.npnct02.log, S.npnct11.log, PubDate.last100.log, H.npnct05.log, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct19.log, H.npnct13.log, A.has.http, A.npnct03.log, A.npnct02.log, A.npnct18.log, A.npnct20.log, A.has.year.colon, S.has.year.colon, A.npnct22.log, S.npnct22.log, H.npnct02.log, A.presid.log, S.presid.log, S.npnct15.log, A.npnct06.log, S.npnct06.log, A.npnct15.log, H.npnct14.log, S.take.log, A.take.log, PubDate.minute.fctr, S.new.log, A.new.log, S.npnct13.log, A.npnct13.log, PubDate.wkday.fctr, S.npnct30.log, A.npnct30.log, S.day.log, A.day.log, H.X2014.log, A.show.log, S.show.log, A.npnct14.log, A.report.log, S.report.log, A.year.log, S.year.log, H.npnct04.log, A.share.log, S.share.log, S.compani.log, A.compani.log, H.new.log, S.npnct14.log, A.first.log, S.first.log, S.time.log, A.time.log, H.newyork.log, A.articl.log, S.articl.log, S.will.log, A.will.log, H.npnct15.log, A.newyork.log, S.newyork.log, H.day.log, A.npnct04.log, S.npnct04.log, H.today.log, H.report.log, H.X2015.log, S.npnct16.log, A.intern.log, S.intern.log, A.npnct16.log, H.daili.log, H.week.log, H.has.year.colon, H.fashion.log, H.npnct16.log, A.fashion.log, S.fashion.log, A.week.log, S.week.log, H.npnct30.log, S.npnct12.log, A.npnct12.log, H.ndgts.log, S.ndgts.log, A.ndgts.log, H.nuppr.log, H.nchrs.log, H.nwrds.log, H.nwrds.unq.log, A.nchrs.log, S.nchrs.log, A.nwrds.log, S.nwrds.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, A.nuppr.log
## max.nTuningRuns max.auc.fit opt.prob.threshold.fit max.f.score.fit
## 1 0 0.5000000 0.5 0.0000000
## 2 0 0.5007516 0.1 0.2867534
## 3 0 0.5000000 0.5 0.0000000
## 4 0 0.5000000 0.5 0.0000000
## 5 1 0.5000000 0.5 0.0000000
## 6 1 0.7073742 0.2 0.3986014
## 7 1 0.7922820 0.3 0.4665885
## 8 1 0.9487497 0.3 0.7441860
## 9 1 0.8237256 0.9 0.6965562
## 10 3 0.7277461 0.7 0.5978351
## 11 3 1.0000000 0.5 1.0000000
## max.Accuracy.fit max.Kappa.fit max.auc.OOB opt.prob.threshold.OOB
## 1 0.8326257 0.0000000000 0.5000000 0.5
## 2 0.1673743 0.0000000000 0.4909227 0.1
## 3 0.8326257 0.0000000000 0.5000000 0.5
## 4 0.8326257 0.0000000000 0.5000000 0.5
## 5 0.8326258 0.0000000000 0.5000000 0.5
## 6 0.8324022 -0.0004459345 0.7102060 0.2
## 7 0.8402235 0.0995997038 0.7766863 0.3
## 8 0.8929586 0.6067690411 0.9226367 0.3
## 9 0.8634728 0.5545248700 0.8196249 0.9
## 10 0.8934084 0.5566658958 0.7084504 0.7
## 11 0.9054749 0.6290019836 0.9189958 0.3
## max.f.score.OOB max.Accuracy.OOB max.Kappa.OOB
## 1 0.0000000 0.8327662 0.0000000
## 2 0.2865473 0.1672338 0.0000000
## 3 0.0000000 0.8327662 0.0000000
## 4 0.0000000 0.8327662 0.0000000
## 5 0.0000000 0.8327662 0.0000000
## 6 0.3880266 0.7316480 0.2283681
## 7 0.4595635 0.7953330 0.3354449
## 8 0.7252747 0.9027710 0.6664245
## 9 0.6892655 0.8930481 0.6247359
## 10 0.5650558 0.8862421 0.5054039
## 11 0.6810229 0.8847837 0.6111869
## inv.elapsedtime.everything inv.elapsedtime.final inv.aic.fit
## 1 1.485884101 3.333333e+02 NA
## 2 2.958579882 1.000000e+03 NA
## 3 1.517450683 1.851852e+01 NA
## 4 1.680672269 1.886792e+01 NA
## 5 0.836120401 1.886792e+01 NA
## 6 0.891265597 1.298701e+01 2.692079e-04
## 7 0.286615076 1.222494e+00 2.924569e-04
## 8 0.066849388 2.134472e-01 4.777963e-04
## 9 0.042551381 1.372307e-01 2.945615e-05
## 10 0.093896714 5.197505e-01 NA
## 11 0.003714545 1.484891e-02 NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 11. Consider specifying shapes manually. if you must have them.## Warning: Removed 5 rows containing missing values (geom_path).## Warning: Removed 74 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 11. Consider specifying shapes manually. if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
#print(mltdCI_models_df)
# castCI_models_df <- dcast(mltdCI_models_df, value ~ type, fun.aggregate=sum)
# print(castCI_models_df)
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Stacking not well defined when ymin != 0dev.off()## quartz_off_screen
## 2print(gp)## Warning: Stacking not well defined when ymin != 0
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)
[, c("model_id", glb_model_evl_criteria,
ifelse(glb_is_classification && glb_is_binomial,
"opt.prob.threshold.OOB", NULL))])## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 8 Low.cor.X.glm 0.9027710 0.9226367 0.6664245
## 9 All.X.glm 0.8930481 0.8196249 0.6247359
## 10 All.X.no.rnorm.rpart 0.8862421 0.7084504 0.5054039
## 11 All.X.no.rnorm.rf 0.8847837 0.9189958 0.6111869
## 1 MFO.myMFO_classfr 0.8327662 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.8327662 0.5000000 0.0000000
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.8327662 0.5000000 0.0000000
## 5 Max.cor.Y.rpart 0.8327662 0.5000000 0.0000000
## 7 Interact.High.cor.Y.glm 0.7953330 0.7766863 0.3354449
## 6 Max.cor.Y.glm 0.7316480 0.7102060 0.2283681
## 2 Random.myrandom_classfr 0.1672338 0.4909227 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 8 2092.942 0.3
## 9 33948.772 0.9
## 10 NA 0.7
## 11 NA 0.3
## 1 NA 0.5
## 3 NA 0.5
## 4 NA 0.5
## 5 NA 0.5
## 7 3419.307 0.3
## 6 3714.601 0.2
## 2 NA 0.1print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 11. Consider specifying shapes manually. if you must have them.## Warning: Removed 33 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 11. Consider specifying shapes manually. if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.auc.OOB - max.Kappa.OOB + min.aic.fit -
## opt.prob.threshold.OOBprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: Low.cor.X.glm"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
}
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])## Warning: not plotting observations with leverage one:
## 1143, 1977, 2501, 2502, 3637, 4105, 4408
## Warning: not plotting observations with leverage one:
## 1143, 1977, 2501, 2502, 3637, 4105, 4408
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7515 -0.3148 -0.1351 0.0000 3.5187
##
## Coefficients: (15 not defined because of singularities)
## Estimate
## (Intercept) -4.260e+00
## WordCount.log 1.098e+00
## `PubDate.hour.fctr(7.67,15.3]` 8.798e-02
## `PubDate.hour.fctr(15.3,23]` 2.505e-01
## H.npnct21.log 1.506e+00
## PubDate.wkend -2.575e-01
## A.npnct21.log 1.446e+00
## H.npnct09.log 2.087e+00
## PubDate.last10.log 2.386e-01
## PubDate.last1.log -4.989e-02
## S.npnct01.log 1.950e+00
## S.can.log -7.442e-01
## H.npnct17.log 1.000e+00
## S.npnct25.log NA
## H.has.ebola -3.483e-01
## S.make.log -4.046e-01
## H.npnct01.log -1.305e+00
## .rnorm -5.272e-03
## A.npnct23.log -6.497e+15
## A.npnct25.log NA
## H.npnct12.log 4.260e-01
## `myCategory.fctrForeign#World#Asia Pacific` -4.060e+00
## `myCategory.fctr#Multimedia#` -4.337e+00
## `myCategory.fctrCulture#Arts#` -2.850e+00
## `myCategory.fctrBusiness#Business Day#Dealbook` -2.405e+00
## myCategory.fctrmyOther -1.984e+01
## `myCategory.fctrBusiness#Technology#` -1.836e+00
## `myCategory.fctrBusiness#Crosswords/Games#` 8.832e-01
## `myCategory.fctrTStyle##` -4.200e+00
## `myCategory.fctrForeign#World#` -1.213e+01
## `myCategory.fctrOpEd#Opinion#` 6.999e-01
## `myCategory.fctrStyles##Fashion` -2.669e+01
## `myCategory.fctr#Opinion#Room For Debate` -5.515e+00
## `myCategory.fctr#U.S.#Education` -2.212e+01
## `myCategory.fctr##` -2.718e+00
## `myCategory.fctrMetro#N.Y. / Region#` -1.877e+00
## `myCategory.fctrBusiness#Business Day#Small Business` -4.512e+00
## `myCategory.fctrStyles#U.S.#` -4.994e-01
## `myCategory.fctrTravel#Travel#` -4.033e+00
## `myCategory.fctr#Opinion#The Public Editor` 1.218e+00
## H.npnct03.log NA
## S.state.log 2.443e+14
## A.state.log -2.443e+14
## S.one.log 3.281e+01
## A.one.log -3.320e+01
## A.said.log 8.717e-01
## S.said.log NA
## A.npnct17.log -3.669e-01
## S.npnct17.log NA
## S.npnct08.log 1.817e+01
## A.npnct08.log NA
## S.npnct09.log -1.664e+01
## A.npnct09.log NA
## A.npnct27.log 2.443e+14
## A.npnct11.log NA
## H.npnct11.log -3.110e+01
## H.npnct22.log -1.868e+00
## S.npnct02.log -2.462e+01
## S.npnct11.log NA
## PubDate.last100.log 2.272e-02
## H.npnct05.log -3.408e+01
## `PubDate.date.fctr(7,13]` -3.726e-02
## `PubDate.date.fctr(13,19]` -1.508e-01
## `PubDate.date.fctr(19,25]` -9.796e-02
## `PubDate.date.fctr(25,31]` 1.253e-01
## `PubDate.second.fctr(14.8,29.5]` 8.011e-02
## `PubDate.second.fctr(29.5,44.2]` -1.157e-02
## `PubDate.second.fctr(44.2,59.1]` -2.946e-01
## H.npnct07.log 2.201e-01
## A.npnct07.log -3.585e+01
## S.npnct07.log NA
## S.npnct03.log -3.765e+01
## A.npnct19.log -7.406e+00
## H.npnct13.log 3.428e-01
## A.has.http NA
## A.npnct03.log NA
## A.npnct02.log NA
## A.npnct18.log 4.430e+00
## A.npnct20.log NA
## A.has.year.colon -1.906e+01
## S.has.year.colon NA
## S.npnct22.log -3.387e+01
## H.npnct02.log -2.358e+01
## S.presid.log 4.570e-01
## S.npnct15.log 1.313e+01
## S.npnct06.log 3.211e-02
## A.npnct15.log -1.231e+01
## H.npnct14.log -2.163e-01
## S.take.log -6.539e-01
## `PubDate.minute.fctr(14.8,29.5]` -1.175e-01
## `PubDate.minute.fctr(29.5,44.2]` -1.994e-01
## `PubDate.minute.fctr(44.2,59.1]` 6.085e-02
## S.new.log 1.426e-02
## S.npnct13.log -1.447e-01
## PubDate.wkday.fctr1 -5.031e-01
## PubDate.wkday.fctr2 -1.130e+00
## PubDate.wkday.fctr3 -7.418e-01
## PubDate.wkday.fctr4 -9.712e-01
## PubDate.wkday.fctr5 -8.478e-01
## PubDate.wkday.fctr6 -1.317e+00
## S.npnct30.log -2.088e+01
## S.day.log -1.872e-01
## H.X2014.log -1.054e+00
## S.show.log -6.204e-01
## A.npnct14.log 9.695e-01
## S.report.log -1.293e+00
## S.year.log -3.837e-01
## H.npnct04.log -1.874e+00
## S.share.log -9.823e-01
## S.compani.log -4.461e-01
## H.new.log -8.862e-01
## S.first.log -1.510e-01
## S.time.log -2.793e-01
## H.newyork.log 1.200e-01
## S.articl.log -1.955e-01
## S.will.log -5.070e-01
## H.npnct15.log -3.039e+01
## S.newyork.log 1.082e+00
## H.day.log -1.155e+00
## S.npnct04.log -1.149e+00
## H.today.log -3.444e+00
## H.report.log -7.682e-01
## S.npnct16.log 4.628e-01
## S.intern.log -9.615e-01
## H.daili.log -1.370e+01
## H.week.log -6.305e-01
## H.npnct16.log -2.355e-01
## S.fashion.log -3.088e+01
## S.week.log -2.581e-01
## H.npnct30.log -1.576e-01
## S.npnct12.log -1.554e-01
## H.ndgts.log 4.681e-01
## S.ndgts.log -3.416e-01
## H.nuppr.log 1.271e+00
## H.nchrs.log -9.599e-01
## H.nwrds.log -7.825e-01
## A.nchrs.log 2.540e-01
## A.nwrds.log 8.726e-01
## A.nwrds.unq.log -1.618e+00
## S.nuppr.log -6.755e-01
## Std. Error
## (Intercept) 2.125e+00
## WordCount.log 8.980e-02
## `PubDate.hour.fctr(7.67,15.3]` 2.464e-01
## `PubDate.hour.fctr(15.3,23]` 2.508e-01
## H.npnct21.log 3.163e-01
## PubDate.wkend 4.455e-01
## A.npnct21.log 3.294e-01
## H.npnct09.log 7.189e-01
## PubDate.last10.log 1.258e-01
## PubDate.last1.log 4.379e-02
## S.npnct01.log 1.748e+00
## S.can.log 4.617e-01
## H.npnct17.log 5.705e-01
## S.npnct25.log NA
## H.has.ebola 4.452e-01
## S.make.log 4.226e-01
## H.npnct01.log 1.255e+00
## .rnorm 6.323e-02
## A.npnct23.log 6.846e+07
## A.npnct25.log NA
## H.npnct12.log 2.088e-01
## `myCategory.fctrForeign#World#Asia Pacific` 6.372e-01
## `myCategory.fctr#Multimedia#` 7.849e-01
## `myCategory.fctrCulture#Arts#` 3.664e-01
## `myCategory.fctrBusiness#Business Day#Dealbook` 3.037e-01
## myCategory.fctrmyOther 1.737e+03
## `myCategory.fctrBusiness#Technology#` 3.208e-01
## `myCategory.fctrBusiness#Crosswords/Games#` 4.971e-01
## `myCategory.fctrTStyle##` 4.932e-01
## `myCategory.fctrForeign#World#` 4.026e+01
## `myCategory.fctrOpEd#Opinion#` 2.930e-01
## `myCategory.fctrStyles##Fashion` 3.319e+04
## `myCategory.fctr#Opinion#Room For Debate` 6.235e-01
## `myCategory.fctr#U.S.#Education` 1.105e+03
## `myCategory.fctr##` 2.858e-01
## `myCategory.fctrMetro#N.Y. / Region#` 4.681e-01
## `myCategory.fctrBusiness#Business Day#Small Business` 6.882e-01
## `myCategory.fctrStyles#U.S.#` 3.342e-01
## `myCategory.fctrTravel#Travel#` 1.073e+00
## `myCategory.fctr#Opinion#The Public Editor` 1.193e+00
## H.npnct03.log NA
## S.state.log 2.825e+14
## A.state.log 2.825e+14
## S.one.log 5.072e+05
## A.one.log 5.072e+05
## A.said.log 4.127e-01
## S.said.log NA
## A.npnct17.log 1.288e+00
## S.npnct17.log NA
## S.npnct08.log 2.523e+05
## A.npnct08.log NA
## S.npnct09.log 2.523e+05
## A.npnct09.log NA
## A.npnct27.log 2.825e+14
## A.npnct11.log NA
## H.npnct11.log 4.926e+05
## H.npnct22.log 5.145e+05
## S.npnct02.log 3.321e+05
## S.npnct11.log NA
## PubDate.last100.log 4.592e-02
## H.npnct05.log 1.920e+05
## `PubDate.date.fctr(7,13]` 1.952e-01
## `PubDate.date.fctr(13,19]` 1.930e-01
## `PubDate.date.fctr(19,25]` 1.898e-01
## `PubDate.date.fctr(25,31]` 2.031e-01
## `PubDate.second.fctr(14.8,29.5]` 1.728e-01
## `PubDate.second.fctr(29.5,44.2]` 1.697e-01
## `PubDate.second.fctr(44.2,59.1]` 1.771e-01
## H.npnct07.log 1.852e-01
## A.npnct07.log 2.252e+05
## S.npnct07.log NA
## S.npnct03.log 1.704e+05
## A.npnct19.log 3.311e+06
## H.npnct13.log 3.097e-01
## A.has.http NA
## A.npnct03.log NA
## A.npnct02.log NA
## A.npnct18.log 1.127e+06
## A.npnct20.log NA
## A.has.year.colon 6.842e+04
## S.has.year.colon NA
## S.npnct22.log 1.436e+05
## H.npnct02.log 9.238e+04
## S.presid.log 4.670e-01
## S.npnct15.log 1.786e+06
## S.npnct06.log 1.520e+00
## A.npnct15.log 1.786e+06
## H.npnct14.log 1.961e-01
## S.take.log 5.543e-01
## `PubDate.minute.fctr(14.8,29.5]` 1.809e-01
## `PubDate.minute.fctr(29.5,44.2]` 1.747e-01
## `PubDate.minute.fctr(44.2,59.1]` 1.815e-01
## S.new.log 3.091e-01
## S.npnct13.log 1.981e-01
## PubDate.wkday.fctr1 5.211e-01
## PubDate.wkday.fctr2 5.670e-01
## PubDate.wkday.fctr3 5.595e-01
## PubDate.wkday.fctr4 5.530e-01
## PubDate.wkday.fctr5 5.593e-01
## PubDate.wkday.fctr6 4.646e-01
## S.npnct30.log 3.906e+04
## S.day.log 6.270e-01
## H.X2014.log 1.435e+00
## S.show.log 6.103e-01
## A.npnct14.log 2.615e-01
## S.report.log 6.076e-01
## S.year.log 4.567e-01
## H.npnct04.log 9.402e-01
## S.share.log 6.562e-01
## S.compani.log 4.137e-01
## H.new.log 6.220e-01
## S.first.log 6.227e-01
## S.time.log 4.609e-01
## H.newyork.log 7.020e-01
## S.articl.log 1.147e+00
## S.will.log 3.714e-01
## H.npnct15.log 4.316e+04
## S.newyork.log 5.143e-01
## H.day.log 1.044e+00
## S.npnct04.log 6.879e-01
## H.today.log 9.766e-01
## H.report.log 1.007e+00
## S.npnct16.log 4.770e-01
## S.intern.log 1.208e+00
## H.daili.log 1.122e+02
## H.week.log 9.343e-01
## H.npnct16.log 2.848e-01
## S.fashion.log 2.957e+04
## S.week.log 4.751e-01
## H.npnct30.log 1.714e+00
## S.npnct12.log 1.429e-01
## H.ndgts.log 2.474e-01
## S.ndgts.log 1.543e-01
## H.nuppr.log 4.208e-01
## H.nchrs.log 4.345e-01
## H.nwrds.log 4.444e-01
## A.nchrs.log 5.061e-01
## A.nwrds.log 1.647e+00
## A.nwrds.unq.log 1.587e+00
## S.nuppr.log 1.558e-01
## z value Pr(>|z|)
## (Intercept) -2.004e+00 0.045039
## WordCount.log 1.223e+01 < 2e-16
## `PubDate.hour.fctr(7.67,15.3]` 3.570e-01 0.721020
## `PubDate.hour.fctr(15.3,23]` 9.990e-01 0.317879
## H.npnct21.log 4.763e+00 1.91e-06
## PubDate.wkend -5.780e-01 0.563328
## A.npnct21.log 4.390e+00 1.13e-05
## H.npnct09.log 2.903e+00 0.003692
## PubDate.last10.log 1.896e+00 0.057944
## PubDate.last1.log -1.139e+00 0.254600
## S.npnct01.log 1.116e+00 0.264568
## S.can.log -1.612e+00 0.107046
## H.npnct17.log 1.753e+00 0.079565
## S.npnct25.log NA NA
## H.has.ebola -7.820e-01 0.434109
## S.make.log -9.570e-01 0.338350
## H.npnct01.log -1.040e+00 0.298319
## .rnorm -8.300e-02 0.933553
## A.npnct23.log -9.491e+07 < 2e-16
## A.npnct25.log NA NA
## H.npnct12.log 2.040e+00 0.041372
## `myCategory.fctrForeign#World#Asia Pacific` -6.372e+00 1.87e-10
## `myCategory.fctr#Multimedia#` -5.525e+00 3.30e-08
## `myCategory.fctrCulture#Arts#` -7.778e+00 7.37e-15
## `myCategory.fctrBusiness#Business Day#Dealbook` -7.920e+00 2.37e-15
## myCategory.fctrmyOther -1.100e-02 0.990884
## `myCategory.fctrBusiness#Technology#` -5.724e+00 1.04e-08
## `myCategory.fctrBusiness#Crosswords/Games#` 1.777e+00 0.075618
## `myCategory.fctrTStyle##` -8.516e+00 < 2e-16
## `myCategory.fctrForeign#World#` -3.010e-01 0.763229
## `myCategory.fctrOpEd#Opinion#` 2.389e+00 0.016888
## `myCategory.fctrStyles##Fashion` -1.000e-03 0.999358
## `myCategory.fctr#Opinion#Room For Debate` -8.846e+00 < 2e-16
## `myCategory.fctr#U.S.#Education` -2.000e-02 0.984027
## `myCategory.fctr##` -9.512e+00 < 2e-16
## `myCategory.fctrMetro#N.Y. / Region#` -4.010e+00 6.07e-05
## `myCategory.fctrBusiness#Business Day#Small Business` -6.556e+00 5.51e-11
## `myCategory.fctrStyles#U.S.#` -1.494e+00 0.135116
## `myCategory.fctrTravel#Travel#` -3.760e+00 0.000170
## `myCategory.fctr#Opinion#The Public Editor` 1.021e+00 0.307391
## H.npnct03.log NA NA
## S.state.log 8.650e-01 0.387232
## A.state.log -8.650e-01 0.387232
## S.one.log 0.000e+00 0.999948
## A.one.log 0.000e+00 0.999948
## A.said.log 2.112e+00 0.034659
## S.said.log NA NA
## A.npnct17.log -2.850e-01 0.775847
## S.npnct17.log NA NA
## S.npnct08.log 0.000e+00 0.999943
## A.npnct08.log NA NA
## S.npnct09.log 0.000e+00 0.999947
## A.npnct09.log NA NA
## A.npnct27.log 8.650e-01 0.387232
## A.npnct11.log NA NA
## H.npnct11.log 0.000e+00 0.999950
## H.npnct22.log 0.000e+00 0.999997
## S.npnct02.log 0.000e+00 0.999941
## S.npnct11.log NA NA
## PubDate.last100.log 4.950e-01 0.620815
## H.npnct05.log 0.000e+00 0.999858
## `PubDate.date.fctr(7,13]` -1.910e-01 0.848639
## `PubDate.date.fctr(13,19]` -7.810e-01 0.434583
## `PubDate.date.fctr(19,25]` -5.160e-01 0.605781
## `PubDate.date.fctr(25,31]` 6.170e-01 0.537249
## `PubDate.second.fctr(14.8,29.5]` 4.640e-01 0.642967
## `PubDate.second.fctr(29.5,44.2]` -6.800e-02 0.945649
## `PubDate.second.fctr(44.2,59.1]` -1.663e+00 0.096295
## H.npnct07.log 1.188e+00 0.234838
## A.npnct07.log 0.000e+00 0.999873
## S.npnct07.log NA NA
## S.npnct03.log 0.000e+00 0.999824
## A.npnct19.log 0.000e+00 0.999998
## H.npnct13.log 1.107e+00 0.268403
## A.has.http NA NA
## A.npnct03.log NA NA
## A.npnct02.log NA NA
## A.npnct18.log 0.000e+00 0.999997
## A.npnct20.log NA NA
## A.has.year.colon 0.000e+00 0.999778
## S.has.year.colon NA NA
## S.npnct22.log 0.000e+00 0.999812
## H.npnct02.log 0.000e+00 0.999796
## S.presid.log 9.790e-01 0.327731
## S.npnct15.log 0.000e+00 0.999994
## S.npnct06.log 2.100e-02 0.983146
## A.npnct15.log 0.000e+00 0.999995
## H.npnct14.log -1.103e+00 0.270021
## S.take.log -1.180e+00 0.238180
## `PubDate.minute.fctr(14.8,29.5]` -6.490e-01 0.516016
## `PubDate.minute.fctr(29.5,44.2]` -1.141e+00 0.253727
## `PubDate.minute.fctr(44.2,59.1]` 3.350e-01 0.737411
## S.new.log 4.600e-02 0.963189
## S.npnct13.log -7.300e-01 0.465188
## PubDate.wkday.fctr1 -9.650e-01 0.334319
## PubDate.wkday.fctr2 -1.993e+00 0.046307
## PubDate.wkday.fctr3 -1.326e+00 0.184906
## PubDate.wkday.fctr4 -1.756e+00 0.079020
## PubDate.wkday.fctr5 -1.516e+00 0.129572
## PubDate.wkday.fctr6 -2.834e+00 0.004598
## S.npnct30.log -1.000e-03 0.999573
## S.day.log -2.990e-01 0.765265
## H.X2014.log -7.350e-01 0.462577
## S.show.log -1.017e+00 0.309388
## A.npnct14.log 3.707e+00 0.000209
## S.report.log -2.128e+00 0.033373
## S.year.log -8.400e-01 0.400871
## H.npnct04.log -1.994e+00 0.046206
## S.share.log -1.497e+00 0.134392
## S.compani.log -1.078e+00 0.280906
## H.new.log -1.425e+00 0.154226
## S.first.log -2.430e-01 0.808364
## S.time.log -6.060e-01 0.544586
## H.newyork.log 1.710e-01 0.864293
## S.articl.log -1.700e-01 0.864648
## S.will.log -1.365e+00 0.172295
## H.npnct15.log -1.000e-03 0.999438
## S.newyork.log 2.103e+00 0.035459
## H.day.log -1.106e+00 0.268689
## S.npnct04.log -1.670e+00 0.094837
## H.today.log -3.527e+00 0.000421
## H.report.log -7.630e-01 0.445472
## S.npnct16.log 9.700e-01 0.331910
## S.intern.log -7.960e-01 0.426005
## H.daili.log -1.220e-01 0.902775
## H.week.log -6.750e-01 0.499793
## H.npnct16.log -8.270e-01 0.408408
## S.fashion.log -1.000e-03 0.999167
## S.week.log -5.430e-01 0.586991
## H.npnct30.log -9.200e-02 0.926752
## S.npnct12.log -1.088e+00 0.276810
## H.ndgts.log 1.892e+00 0.058486
## S.ndgts.log -2.214e+00 0.026796
## H.nuppr.log 3.020e+00 0.002528
## H.nchrs.log -2.209e+00 0.027161
## H.nwrds.log -1.761e+00 0.078289
## A.nchrs.log 5.020e-01 0.615783
## A.nwrds.log 5.300e-01 0.596341
## A.nwrds.unq.log -1.019e+00 0.308053
## S.nuppr.log -4.337e+00 1.45e-05
##
## (Intercept) *
## WordCount.log ***
## `PubDate.hour.fctr(7.67,15.3]`
## `PubDate.hour.fctr(15.3,23]`
## H.npnct21.log ***
## PubDate.wkend
## A.npnct21.log ***
## H.npnct09.log **
## PubDate.last10.log .
## PubDate.last1.log
## S.npnct01.log
## S.can.log
## H.npnct17.log .
## S.npnct25.log
## H.has.ebola
## S.make.log
## H.npnct01.log
## .rnorm
## A.npnct23.log ***
## A.npnct25.log
## H.npnct12.log *
## `myCategory.fctrForeign#World#Asia Pacific` ***
## `myCategory.fctr#Multimedia#` ***
## `myCategory.fctrCulture#Arts#` ***
## `myCategory.fctrBusiness#Business Day#Dealbook` ***
## myCategory.fctrmyOther
## `myCategory.fctrBusiness#Technology#` ***
## `myCategory.fctrBusiness#Crosswords/Games#` .
## `myCategory.fctrTStyle##` ***
## `myCategory.fctrForeign#World#`
## `myCategory.fctrOpEd#Opinion#` *
## `myCategory.fctrStyles##Fashion`
## `myCategory.fctr#Opinion#Room For Debate` ***
## `myCategory.fctr#U.S.#Education`
## `myCategory.fctr##` ***
## `myCategory.fctrMetro#N.Y. / Region#` ***
## `myCategory.fctrBusiness#Business Day#Small Business` ***
## `myCategory.fctrStyles#U.S.#`
## `myCategory.fctrTravel#Travel#` ***
## `myCategory.fctr#Opinion#The Public Editor`
## H.npnct03.log
## S.state.log
## A.state.log
## S.one.log
## A.one.log
## A.said.log *
## S.said.log
## A.npnct17.log
## S.npnct17.log
## S.npnct08.log
## A.npnct08.log
## S.npnct09.log
## A.npnct09.log
## A.npnct27.log
## A.npnct11.log
## H.npnct11.log
## H.npnct22.log
## S.npnct02.log
## S.npnct11.log
## PubDate.last100.log
## H.npnct05.log
## `PubDate.date.fctr(7,13]`
## `PubDate.date.fctr(13,19]`
## `PubDate.date.fctr(19,25]`
## `PubDate.date.fctr(25,31]`
## `PubDate.second.fctr(14.8,29.5]`
## `PubDate.second.fctr(29.5,44.2]`
## `PubDate.second.fctr(44.2,59.1]` .
## H.npnct07.log
## A.npnct07.log
## S.npnct07.log
## S.npnct03.log
## A.npnct19.log
## H.npnct13.log
## A.has.http
## A.npnct03.log
## A.npnct02.log
## A.npnct18.log
## A.npnct20.log
## A.has.year.colon
## S.has.year.colon
## S.npnct22.log
## H.npnct02.log
## S.presid.log
## S.npnct15.log
## S.npnct06.log
## A.npnct15.log
## H.npnct14.log
## S.take.log
## `PubDate.minute.fctr(14.8,29.5]`
## `PubDate.minute.fctr(29.5,44.2]`
## `PubDate.minute.fctr(44.2,59.1]`
## S.new.log
## S.npnct13.log
## PubDate.wkday.fctr1
## PubDate.wkday.fctr2 *
## PubDate.wkday.fctr3
## PubDate.wkday.fctr4 .
## PubDate.wkday.fctr5
## PubDate.wkday.fctr6 **
## S.npnct30.log
## S.day.log
## H.X2014.log
## S.show.log
## A.npnct14.log ***
## S.report.log *
## S.year.log
## H.npnct04.log *
## S.share.log
## S.compani.log
## H.new.log
## S.first.log
## S.time.log
## H.newyork.log
## S.articl.log
## S.will.log
## H.npnct15.log
## S.newyork.log *
## H.day.log
## S.npnct04.log .
## H.today.log ***
## H.report.log
## S.npnct16.log
## S.intern.log
## H.daili.log
## H.week.log
## H.npnct16.log
## S.fashion.log
## S.week.log
## H.npnct30.log
## S.npnct12.log
## H.ndgts.log .
## S.ndgts.log *
## H.nuppr.log **
## H.nchrs.log *
## H.nwrds.log .
## A.nchrs.log
## A.nwrds.log
## A.nwrds.unq.log
## S.nuppr.log ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1842.9 on 4350 degrees of freedom
## AIC: 2092.9
##
## Number of Fisher Scoring iterations: 25## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(glb_rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
}
return(df)
}
glb_OOBent_df <- glb_get_predictions(df=glb_OOBent_df, glb_sel_mdl_id, glb_rsp_var_out)## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleadingpredct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
glb_OOBent_df[, predct_accurate_var_name] <-
(glb_OOBent_df[, glb_rsp_var] ==
glb_OOBent_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
glb_feats_df <-
mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_sel_mdl, glb_fitent_df)
glb_feats_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_feats_df$importance
print(glb_feats_df)## id cor.y exclude.as.feat
## A.npnct23.log A.npnct23.log 1.537569e-02 FALSE
## WordCount.log WordCount.log 2.649604e-01 FALSE
## myCategory.fctr myCategory.fctr 1.234541e-02 FALSE
## H.npnct21.log H.npnct21.log 1.283641e-01 FALSE
## A.npnct21.log A.npnct21.log 5.482747e-02 FALSE
## S.nuppr.log S.nuppr.log -2.718459e-01 FALSE
## A.npnct14.log A.npnct14.log -4.999563e-02 FALSE
## H.today.log H.today.log -6.372306e-02 FALSE
## H.nuppr.log H.nuppr.log -1.278085e-01 FALSE
## H.npnct09.log H.npnct09.log 5.375262e-02 FALSE
## PubDate.wkday.fctr PubDate.wkday.fctr -3.980129e-02 FALSE
## S.ndgts.log S.ndgts.log -1.242046e-01 FALSE
## H.nchrs.log H.nchrs.log -1.710624e-01 FALSE
## S.report.log S.report.log -5.032801e-02 FALSE
## A.said.log A.said.log 3.735051e-04 FALSE
## S.newyork.log S.newyork.log -6.219997e-02 FALSE
## H.npnct12.log H.npnct12.log 1.333613e-02 FALSE
## H.npnct04.log H.npnct04.log -5.126277e-02 FALSE
## PubDate.last10.log PubDate.last10.log 4.931702e-02 FALSE
## H.ndgts.log H.ndgts.log -1.196633e-01 FALSE
## H.nwrds.log H.nwrds.log -2.006864e-01 FALSE
## H.npnct17.log H.npnct17.log 3.039622e-02 FALSE
## S.npnct04.log S.npnct04.log -6.294642e-02 FALSE
## PubDate.second.fctr PubDate.second.fctr -1.187946e-02 FALSE
## S.can.log S.can.log 3.077833e-02 FALSE
## S.share.log S.share.log -5.138139e-02 FALSE
## H.new.log H.new.log -5.313316e-02 FALSE
## S.will.log S.will.log -6.103349e-02 FALSE
## H.npnct07.log H.npnct07.log -1.201741e-02 FALSE
## S.take.log S.take.log -2.569295e-02 FALSE
## PubDate.minute.fctr PubDate.minute.fctr -3.407385e-02 FALSE
## PubDate.last1.log PubDate.last1.log 4.635751e-02 FALSE
## S.npnct01.log S.npnct01.log 3.093101e-02 FALSE
## H.npnct13.log H.npnct13.log -1.305305e-02 FALSE
## H.day.log H.day.log -6.272898e-02 FALSE
## H.npnct14.log H.npnct14.log -2.524770e-02 FALSE
## S.npnct12.log S.npnct12.log -9.158156e-02 FALSE
## S.compani.log S.compani.log -5.261812e-02 FALSE
## H.npnct01.log H.npnct01.log 2.271577e-02 FALSE
## A.nwrds.unq.log A.nwrds.unq.log -2.506012e-01 FALSE
## S.show.log S.show.log -4.897915e-02 FALSE
## PubDate.hour.fctr PubDate.hour.fctr 1.354368e-01 FALSE
## S.presid.log S.presid.log -2.014404e-02 FALSE
## S.npnct16.log S.npnct16.log -6.770952e-02 FALSE
## S.make.log S.make.log 2.334962e-02 FALSE
## S.state.log S.state.log 7.050791e-03 FALSE
## A.state.log A.state.log 6.668101e-03 FALSE
## A.npnct27.log A.npnct27.log -5.547032e-03 FALSE
## S.year.log S.year.log -5.094457e-02 FALSE
## H.npnct16.log H.npnct16.log -8.273237e-02 FALSE
## S.intern.log S.intern.log -6.864274e-02 FALSE
## H.has.ebola H.has.ebola 2.588140e-02 FALSE
## PubDate.date.fctr PubDate.date.fctr -1.164756e-02 FALSE
## H.report.log H.report.log -6.494810e-02 FALSE
## H.X2014.log H.X2014.log -4.620638e-02 FALSE
## S.npnct13.log S.npnct13.log -3.638891e-02 FALSE
## H.week.log H.week.log -7.510522e-02 FALSE
## S.time.log S.time.log -5.759227e-02 FALSE
## PubDate.wkend PubDate.wkend 1.067288e-01 FALSE
## S.week.log S.week.log -8.840293e-02 FALSE
## A.nwrds.log A.nwrds.log -2.450733e-01 FALSE
## A.nchrs.log A.nchrs.log -2.245488e-01 FALSE
## PubDate.last100.log PubDate.last100.log -7.663322e-03 FALSE
## S.day.log S.day.log -4.555421e-02 FALSE
## A.npnct17.log A.npnct17.log -1.587454e-03 FALSE
## S.first.log S.first.log -5.345938e-02 FALSE
## H.newyork.log H.newyork.log -5.797009e-02 FALSE
## S.articl.log S.articl.log -5.952055e-02 FALSE
## H.daili.log H.daili.log -6.919298e-02 FALSE
## H.npnct30.log H.npnct30.log -8.917338e-02 FALSE
## .rnorm .rnorm 1.756172e-02 FALSE
## S.new.log S.new.log -3.483189e-02 FALSE
## S.npnct06.log S.npnct06.log -2.389145e-02 FALSE
## S.fashion.log S.fashion.log -8.724932e-02 FALSE
## H.npnct15.log H.npnct15.log -6.158577e-02 FALSE
## S.npnct30.log S.npnct30.log -4.370037e-02 FALSE
## A.has.year.colon A.has.year.colon -1.755336e-02 FALSE
## H.npnct02.log H.npnct02.log -2.001851e-02 FALSE
## S.npnct22.log S.npnct22.log -1.923169e-02 FALSE
## S.npnct03.log S.npnct03.log -1.240734e-02 FALSE
## H.npnct05.log H.npnct05.log -9.653967e-03 FALSE
## A.npnct07.log A.npnct07.log -1.214357e-02 FALSE
## S.npnct02.log S.npnct02.log -5.547032e-03 FALSE
## S.npnct08.log S.npnct08.log -2.413868e-03 FALSE
## S.npnct09.log S.npnct09.log -3.986882e-03 FALSE
## A.one.log A.one.log 4.368856e-03 FALSE
## S.one.log S.one.log 4.891059e-03 FALSE
## H.npnct11.log H.npnct11.log -5.547032e-03 FALSE
## S.npnct15.log S.npnct15.log -2.121844e-02 FALSE
## A.npnct15.log A.npnct15.log -2.407715e-02 FALSE
## A.npnct18.log A.npnct18.log -1.451467e-02 FALSE
## H.npnct22.log H.npnct22.log -5.547032e-03 FALSE
## A.npnct19.log A.npnct19.log -1.271661e-02 FALSE
## A.articl.log A.articl.log -5.952055e-02 FALSE
## A.can.log A.can.log 3.169296e-02 FALSE
## A.compani.log A.compani.log -5.268413e-02 FALSE
## A.day.log A.day.log -4.581783e-02 FALSE
## A.fashion.log A.fashion.log -8.724932e-02 FALSE
## A.first.log A.first.log -5.345938e-02 FALSE
## A.has.http A.has.http -1.359260e-02 FALSE
## A.intern.log A.intern.log -6.864274e-02 FALSE
## A.make.log A.make.log 2.334962e-02 FALSE
## A.ndgts.log A.ndgts.log -1.249484e-01 FALSE
## A.new.log A.new.log -3.524871e-02 FALSE
## A.newyork.log A.newyork.log -6.219997e-02 FALSE
## A.npnct01.log A.npnct01.log 3.093101e-02 FALSE
## A.npnct02.log A.npnct02.log -1.451467e-02 FALSE
## A.npnct03.log A.npnct03.log -1.359260e-02 FALSE
## A.npnct04.log A.npnct04.log -6.294642e-02 FALSE
## A.npnct05.log A.npnct05.log NA FALSE
## A.npnct06.log A.npnct06.log -2.389145e-02 FALSE
## A.npnct08.log A.npnct08.log -3.258100e-03 FALSE
## A.npnct09.log A.npnct09.log -4.775988e-03 FALSE
## A.npnct10.log A.npnct10.log NA FALSE
## A.npnct11.log A.npnct11.log -5.547032e-03 FALSE
## A.npnct12.log A.npnct12.log -9.183870e-02 FALSE
## A.npnct13.log A.npnct13.log -3.760012e-02 FALSE
## A.npnct16.log A.npnct16.log -6.893301e-02 FALSE
## A.npnct20.log A.npnct20.log -1.451467e-02 FALSE
## A.npnct22.log A.npnct22.log -1.923169e-02 FALSE
## A.npnct24.log A.npnct24.log NA FALSE
## A.npnct25.log A.npnct25.log 1.537569e-02 FALSE
## A.npnct26.log A.npnct26.log -9.890046e-19 FALSE
## A.npnct28.log A.npnct28.log NA FALSE
## A.npnct29.log A.npnct29.log NA FALSE
## A.npnct30.log A.npnct30.log -4.373349e-02 FALSE
## A.npnct31.log A.npnct31.log NA FALSE
## A.npnct32.log A.npnct32.log NA FALSE
## A.nuppr.log A.nuppr.log -2.720962e-01 FALSE
## A.presid.log A.presid.log -2.014404e-02 FALSE
## A.report.log A.report.log -5.032801e-02 FALSE
## A.share.log A.share.log -5.138139e-02 FALSE
## A.show.log A.show.log -4.897915e-02 FALSE
## A.take.log A.take.log -2.601772e-02 FALSE
## A.time.log A.time.log -5.779371e-02 FALSE
## A.week.log A.week.log -8.840293e-02 FALSE
## A.will.log A.will.log -6.147068e-02 FALSE
## A.year.log A.year.log -5.094457e-02 FALSE
## H.fashion.log H.fashion.log -8.204998e-02 FALSE
## H.has.http H.has.http NA FALSE
## H.has.year.colon H.has.year.colon -7.842875e-02 FALSE
## H.npnct03.log H.npnct03.log 9.533020e-03 FALSE
## H.npnct06.log H.npnct06.log 3.190718e-02 FALSE
## H.npnct08.log H.npnct08.log 5.375262e-02 FALSE
## H.npnct10.log H.npnct10.log NA FALSE
## H.npnct18.log H.npnct18.log NA FALSE
## H.npnct19.log H.npnct19.log NA FALSE
## H.npnct20.log H.npnct20.log NA FALSE
## H.npnct23.log H.npnct23.log NA FALSE
## H.npnct24.log H.npnct24.log NA FALSE
## H.npnct25.log H.npnct25.log NA FALSE
## H.npnct26.log H.npnct26.log -9.890046e-19 FALSE
## H.npnct27.log H.npnct27.log NA FALSE
## H.npnct28.log H.npnct28.log NA FALSE
## H.npnct29.log H.npnct29.log NA FALSE
## H.npnct31.log H.npnct31.log NA FALSE
## H.npnct32.log H.npnct32.log NA FALSE
## H.nwrds.unq.log H.nwrds.unq.log -2.044964e-01 FALSE
## H.X2015.log H.X2015.log -6.658489e-02 FALSE
## Popular Popular 1.000000e+00 TRUE
## Popular.fctr Popular.fctr NA TRUE
## PubDate.last1 PubDate.last1 3.592267e-02 TRUE
## PubDate.last10 PubDate.last10 5.398093e-02 TRUE
## PubDate.last100 PubDate.last100 3.989229e-02 TRUE
## PubDate.month.fctr PubDate.month.fctr 1.914874e-02 TRUE
## PubDate.POSIX PubDate.POSIX 1.568326e-02 TRUE
## PubDate.year.fctr PubDate.year.fctr NA FALSE
## PubDate.zoo PubDate.zoo 1.568326e-02 TRUE
## S.has.http S.has.http NA FALSE
## S.has.year.colon S.has.year.colon -1.755336e-02 FALSE
## S.nchrs.log S.nchrs.log -2.246930e-01 FALSE
## S.npnct05.log S.npnct05.log NA FALSE
## S.npnct07.log S.npnct07.log -1.214357e-02 FALSE
## S.npnct10.log S.npnct10.log NA FALSE
## S.npnct11.log S.npnct11.log -5.547032e-03 FALSE
## S.npnct14.log S.npnct14.log -5.332519e-02 FALSE
## S.npnct17.log S.npnct17.log -1.587454e-03 FALSE
## S.npnct18.log S.npnct18.log NA FALSE
## S.npnct19.log S.npnct19.log NA FALSE
## S.npnct20.log S.npnct20.log NA FALSE
## S.npnct21.log S.npnct21.log 5.503894e-02 FALSE
## S.npnct23.log S.npnct23.log 2.760321e-02 FALSE
## S.npnct24.log S.npnct24.log NA FALSE
## S.npnct25.log S.npnct25.log 2.760321e-02 FALSE
## S.npnct26.log S.npnct26.log -9.890046e-19 FALSE
## S.npnct27.log S.npnct27.log NA FALSE
## S.npnct28.log S.npnct28.log NA FALSE
## S.npnct29.log S.npnct29.log NA FALSE
## S.npnct31.log S.npnct31.log NA FALSE
## S.npnct32.log S.npnct32.log NA FALSE
## S.nwrds.log S.nwrds.log -2.453541e-01 FALSE
## S.nwrds.unq.log S.nwrds.unq.log -2.507969e-01 FALSE
## S.said.log S.said.log 3.735051e-04 FALSE
## UniqueID UniqueID 1.182492e-02 TRUE
## WordCount WordCount 2.575265e-01 TRUE
## cor.y.abs cor.high.X freqRatio percentUnique
## A.npnct23.log 1.537569e-02 <NA> 3264.500000 0.04592774
## WordCount.log 2.649604e-01 <NA> 1.266667 24.15799143
## myCategory.fctr 1.234541e-02 <NA> 1.337185 0.30618494
## H.npnct21.log 1.283641e-01 <NA> 14.995098 0.06123699
## A.npnct21.log 5.482747e-02 <NA> 12.798715 0.07654623
## S.nuppr.log 2.718459e-01 <NA> 1.152620 0.33680343
## A.npnct14.log 4.999563e-02 <NA> 4.603330 0.16840171
## H.today.log 6.372306e-02 <NA> 36.757225 0.03061849
## H.nuppr.log 1.278085e-01 <NA> 1.033930 0.29087569
## H.npnct09.log 5.375262e-02 <NA> 111.620690 0.03061849
## PubDate.wkday.fctr 3.980129e-02 <NA> 1.003268 0.10716473
## S.ndgts.log 1.242046e-01 <NA> 10.511247 0.26025720
## H.nchrs.log 1.710624e-01 <NA> 1.023810 1.57685242
## S.report.log 5.032801e-02 <NA> 24.204633 0.06123699
## A.said.log 3.735051e-04 <NA> 25.212851 0.04592774
## S.newyork.log 6.219997e-02 <NA> 15.153465 0.06123699
## H.npnct12.log 1.333613e-02 <NA> 4.937442 0.07654623
## H.npnct04.log 5.126277e-02 <NA> 38.325301 0.04592774
## PubDate.last10.log 4.931702e-02 <NA> 1.666667 79.05695040
## H.ndgts.log 1.196633e-01 <NA> 13.616137 0.18371096
## H.nwrds.log 2.006864e-01 <NA> 1.019119 0.21432945
## H.npnct17.log 3.039622e-02 <NA> 96.104478 0.06123699
## S.npnct04.log 6.294642e-02 <NA> 28.536364 0.07654623
## PubDate.second.fctr 1.187946e-02 <NA> 1.018204 0.06123699
## S.can.log 3.077833e-02 <NA> 26.058091 0.04592774
## S.share.log 5.138139e-02 <NA> 32.654639 0.04592774
## H.new.log 5.313316e-02 <NA> 25.228916 0.04592774
## S.will.log 6.103349e-02 <NA> 11.237288 0.06123699
## H.npnct07.log 1.201741e-02 <NA> 5.437234 0.12247397
## S.take.log 2.569295e-02 <NA> 29.376744 0.04592774
## PubDate.minute.fctr 3.407385e-02 <NA> 1.483365 0.06123699
## PubDate.last1.log 4.635751e-02 <NA> 1.142857 36.49724434
## S.npnct01.log 3.093101e-02 <NA> 309.952381 0.06123699
## H.npnct13.log 1.305305e-02 <NA> 13.126638 0.09185548
## H.day.log 6.272898e-02 <NA> 29.801887 0.04592774
## H.npnct14.log 2.524770e-02 <NA> 22.802326 0.12247397
## S.npnct12.log 9.158156e-02 <NA> 1.660473 0.13778322
## S.compani.log 5.261812e-02 <NA> 18.093842 0.04592774
## H.npnct01.log 2.271577e-02 <NA> 282.913043 0.04592774
## A.nwrds.unq.log 2.506012e-01 <NA> 1.061567 0.55113288
## S.show.log 4.897915e-02 <NA> 30.512077 0.06123699
## PubDate.hour.fctr 1.354368e-01 <NA> 1.835040 0.04592774
## S.presid.log 2.014404e-02 <NA> 26.854701 0.06123699
## S.npnct16.log 6.770952e-02 <NA> 13.647191 0.04592774
## S.make.log 2.334962e-02 <NA> 27.378261 0.04592774
## S.state.log 7.050791e-03 <NA> 30.655340 0.04592774
## A.state.log 6.668101e-03 <NA> 30.502415 0.04592774
## A.npnct27.log 5.547032e-03 <NA> 6531.000000 0.03061849
## S.year.log 5.094457e-02 <NA> 18.456716 0.06123699
## H.npnct16.log 8.273237e-02 <NA> 3.914910 0.04592774
## S.intern.log 6.864274e-02 <NA> 29.801887 0.04592774
## H.has.ebola 2.588140e-02 <NA> 73.227273 0.03061849
## PubDate.date.fctr 1.164756e-02 <NA> 1.021394 0.07654623
## H.report.log 6.494810e-02 <NA> 30.403846 0.03061849
## H.X2014.log 4.620638e-02 <NA> 63.673267 0.03061849
## S.npnct13.log 3.638891e-02 <NA> 5.706263 0.09185548
## H.week.log 7.510522e-02 <NA> 24.818182 0.03061849
## S.time.log 5.759227e-02 <NA> 13.483296 0.04592774
## PubDate.wkend 1.067288e-01 <NA> 9.095827 0.03061849
## S.week.log 8.840293e-02 <NA> 13.278509 0.04592774
## A.nwrds.log 2.450733e-01 <NA> 1.029183 0.59706062
## A.nchrs.log 2.245488e-01 <NA> 1.328571 4.39375383
## PubDate.last100.log 7.663322e-03 <NA> 25.000000 92.19228414
## S.day.log 4.555421e-02 <NA> 24.692913 0.04592774
## A.npnct17.log 1.587454e-03 <NA> 434.133333 0.04592774
## S.first.log 5.345938e-02 <NA> 29.509346 0.04592774
## H.newyork.log 5.797009e-02 <NA> 26.795745 0.03061849
## S.articl.log 5.952055e-02 <NA> 30.863415 0.03061849
## H.daili.log 6.919298e-02 <NA> 41.973684 0.03061849
## H.npnct30.log 8.917338e-02 <NA> 24.123077 0.03061849
## .rnorm 1.756172e-02 <NA> 1.000000 100.00000000
## S.new.log 3.483189e-02 <NA> 10.124573 0.04592774
## S.npnct06.log 2.389145e-02 <NA> 115.642857 0.03061849
## S.fashion.log 8.724932e-02 <NA> 25.737705 0.04592774
## H.npnct15.log 6.158577e-02 <NA> 52.983471 0.03061849
## S.npnct30.log 4.370037e-02 <NA> 134.791667 0.04592774
## A.has.year.colon 1.755336e-02 <NA> 652.200000 0.03061849
## H.npnct02.log 2.001851e-02 <NA> 501.461538 0.03061849
## S.npnct22.log 1.923169e-02 <NA> 543.333333 0.03061849
## S.npnct03.log 1.240734e-02 <NA> 1305.400000 0.03061849
## H.npnct05.log 9.653967e-03 <NA> 543.333333 0.03061849
## A.npnct07.log 1.214357e-02 <NA> 1631.750000 0.04592774
## S.npnct02.log 5.547032e-03 <NA> 6531.000000 0.03061849
## S.npnct08.log 2.413868e-03 <NA> 175.513514 0.04592774
## S.npnct09.log 3.986882e-03 <NA> 175.486486 0.06123699
## A.one.log 4.368856e-03 <NA> 22.773723 0.04592774
## S.one.log 4.891059e-03 <NA> 22.777372 0.04592774
## H.npnct11.log 5.547032e-03 <NA> 6531.000000 0.03061849
## S.npnct15.log 2.121844e-02 <NA> 203.062500 0.04592774
## A.npnct15.log 2.407715e-02 <NA> 196.696970 0.10716473
## A.npnct18.log 1.451467e-02 <NA> 1087.500000 0.04592774
## H.npnct22.log 5.547032e-03 <NA> 6531.000000 0.03061849
## A.npnct19.log 1.271661e-02 <NA> 1631.500000 0.06123699
## A.articl.log 5.952055e-02 S.articl.log 30.863415 0.03061849
## A.can.log 3.169296e-02 S.can.log 26.166667 0.04592774
## A.compani.log 5.268413e-02 S.compani.log 18.147059 0.04592774
## A.day.log 4.581783e-02 S.day.log 24.592157 0.04592774
## A.fashion.log 8.724932e-02 S.fashion.log 25.737705 0.04592774
## A.first.log 5.345938e-02 S.first.log 29.509346 0.04592774
## A.has.http 1.359260e-02 <NA> 1087.666667 0.03061849
## A.intern.log 6.864274e-02 S.intern.log 29.801887 0.04592774
## A.make.log 2.334962e-02 S.make.log 27.378261 0.04592774
## A.ndgts.log 1.249484e-01 S.ndgts.log 10.501022 0.29087569
## A.new.log 3.524871e-02 S.new.log 10.086735 0.04592774
## A.newyork.log 6.219997e-02 S.newyork.log 15.153465 0.06123699
## A.npnct01.log 3.093101e-02 S.npnct01.log 309.952381 0.06123699
## A.npnct02.log 1.451467e-02 <NA> 1087.500000 0.04592774
## A.npnct03.log 1.359260e-02 <NA> 1087.666667 0.03061849
## A.npnct04.log 6.294642e-02 S.npnct04.log 28.536364 0.07654623
## A.npnct05.log NA <NA> 0.000000 0.01530925
## A.npnct06.log 2.389145e-02 S.npnct06.log 115.642857 0.03061849
## A.npnct08.log 3.258100e-03 <NA> 170.868421 0.04592774
## A.npnct09.log 4.775988e-03 <NA> 170.842105 0.06123699
## A.npnct10.log NA <NA> 0.000000 0.01530925
## A.npnct11.log 5.547032e-03 <NA> 6531.000000 0.03061849
## A.npnct12.log 9.183870e-02 S.npnct12.log 1.660473 0.13778322
## A.npnct13.log 3.760012e-02 S.npnct13.log 5.715368 0.12247397
## A.npnct16.log 6.893301e-02 S.npnct16.log 13.482222 0.04592774
## A.npnct20.log 1.451467e-02 <NA> 1087.500000 0.04592774
## A.npnct22.log 1.923169e-02 S.npnct22.log 543.333333 0.03061849
## A.npnct24.log NA <NA> 0.000000 0.01530925
## A.npnct25.log 1.537569e-02 <NA> 3264.500000 0.04592774
## A.npnct26.log 9.890046e-19 <NA> 0.000000 0.01530925
## A.npnct28.log NA <NA> 0.000000 0.01530925
## A.npnct29.log NA <NA> 0.000000 0.01530925
## A.npnct30.log 4.373349e-02 S.npnct30.log 126.862745 0.04592774
## A.npnct31.log NA <NA> 0.000000 0.01530925
## A.npnct32.log NA <NA> 0.000000 0.01530925
## A.nuppr.log 2.720962e-01 S.nuppr.log 1.151308 0.33680343
## A.presid.log 2.014404e-02 S.presid.log 26.854701 0.06123699
## A.report.log 5.032801e-02 S.report.log 24.204633 0.06123699
## A.share.log 5.138139e-02 S.share.log 32.654639 0.04592774
## A.show.log 4.897915e-02 S.show.log 30.512077 0.06123699
## A.take.log 2.601772e-02 S.take.log 29.236111 0.04592774
## A.time.log 5.779371e-02 S.time.log 13.451111 0.04592774
## A.week.log 8.840293e-02 S.week.log 13.278509 0.04592774
## A.will.log 6.147068e-02 S.will.log 11.212406 0.06123699
## A.year.log 5.094457e-02 S.year.log 18.456716 0.06123699
## H.fashion.log 8.204998e-02 H.week.log 28.542986 0.04592774
## H.has.http NA <NA> 0.000000 0.01530925
## H.has.year.colon 7.842875e-02 A.intern.log 32.670103 0.03061849
## H.npnct03.log 9.533020e-03 <NA> 2176.333333 0.03061849
## H.npnct06.log 3.190718e-02 H.npnct17.log 68.935484 0.06123699
## H.npnct08.log 5.375262e-02 H.npnct09.log 111.620690 0.03061849
## H.npnct10.log NA <NA> 0.000000 0.01530925
## H.npnct18.log NA <NA> 0.000000 0.01530925
## H.npnct19.log NA <NA> 0.000000 0.01530925
## H.npnct20.log NA <NA> 0.000000 0.01530925
## H.npnct23.log NA <NA> 0.000000 0.01530925
## H.npnct24.log NA <NA> 0.000000 0.01530925
## H.npnct25.log NA <NA> 0.000000 0.01530925
## H.npnct26.log 9.890046e-19 <NA> 0.000000 0.01530925
## H.npnct27.log NA <NA> 0.000000 0.01530925
## H.npnct28.log NA <NA> 0.000000 0.01530925
## H.npnct29.log NA <NA> 0.000000 0.01530925
## H.npnct31.log NA <NA> 0.000000 0.01530925
## H.npnct32.log NA <NA> 0.000000 0.01530925
## H.nwrds.unq.log 2.044964e-01 H.nuppr.log 1.019071 0.21432945
## H.X2015.log 6.658489e-02 H.npnct15.log 45.326241 0.03061849
## Popular 1.000000e+00 <NA> 4.976212 0.03061849
## Popular.fctr NA <NA> NA NA
## PubDate.last1 3.592267e-02 <NA> 1.142857 36.49724434
## PubDate.last10 5.398093e-02 <NA> 1.666667 79.05695040
## PubDate.last100 3.989229e-02 <NA> 25.000000 92.52908757
## PubDate.month.fctr 1.914874e-02 <NA> 1.017514 0.04592774
## PubDate.POSIX 1.568326e-02 <NA> 1.000000 99.86221678
## PubDate.year.fctr NA <NA> 0.000000 0.01530925
## PubDate.zoo 1.568326e-02 <NA> 1.000000 99.86221678
## S.has.http NA <NA> 0.000000 0.01530925
## S.has.year.colon 1.755336e-02 <NA> 652.200000 0.03061849
## S.nchrs.log 2.246930e-01 A.nchrs.log 1.328571 3.72014697
## S.npnct05.log NA <NA> 0.000000 0.01530925
## S.npnct07.log 1.214357e-02 <NA> 1631.750000 0.04592774
## S.npnct10.log NA <NA> 0.000000 0.01530925
## S.npnct11.log 5.547032e-03 <NA> 6531.000000 0.03061849
## S.npnct14.log 5.332519e-02 A.npnct14.log 4.672000 0.16840171
## S.npnct17.log 1.587454e-03 <NA> 434.133333 0.04592774
## S.npnct18.log NA <NA> 0.000000 0.01530925
## S.npnct19.log NA <NA> 0.000000 0.01530925
## S.npnct20.log NA <NA> 0.000000 0.01530925
## S.npnct21.log 5.503894e-02 A.npnct21.log 12.862366 0.07654623
## S.npnct23.log 2.760321e-02 S.npnct25.log 6531.000000 0.03061849
## S.npnct24.log NA <NA> 0.000000 0.01530925
## S.npnct25.log 2.760321e-02 <NA> 6531.000000 0.03061849
## S.npnct26.log 9.890046e-19 <NA> 0.000000 0.01530925
## S.npnct27.log NA <NA> 0.000000 0.01530925
## S.npnct28.log NA <NA> 0.000000 0.01530925
## S.npnct29.log NA <NA> 0.000000 0.01530925
## S.npnct31.log NA <NA> 0.000000 0.01530925
## S.npnct32.log NA <NA> 0.000000 0.01530925
## S.nwrds.log 2.453541e-01 A.nwrds.log 1.029183 0.45927740
## S.nwrds.unq.log 2.507969e-01 S.nchrs.log 1.061567 0.44396816
## S.said.log 3.735051e-04 <NA> 25.212851 0.04592774
## UniqueID 1.182492e-02 <NA> 1.000000 100.00000000
## WordCount 2.575265e-01 <NA> 2.315789 24.15799143
## zeroVar nzv is.cor.y.abs.low rsp_var_raw id_var
## A.npnct23.log FALSE TRUE TRUE FALSE NA
## WordCount.log FALSE FALSE FALSE FALSE NA
## myCategory.fctr FALSE FALSE TRUE FALSE NA
## H.npnct21.log FALSE FALSE FALSE FALSE NA
## A.npnct21.log FALSE FALSE FALSE FALSE NA
## S.nuppr.log FALSE FALSE FALSE FALSE NA
## A.npnct14.log FALSE FALSE FALSE FALSE NA
## H.today.log FALSE TRUE FALSE FALSE NA
## H.nuppr.log FALSE FALSE FALSE FALSE NA
## H.npnct09.log FALSE TRUE FALSE FALSE NA
## PubDate.wkday.fctr FALSE FALSE FALSE FALSE NA
## S.ndgts.log FALSE FALSE FALSE FALSE NA
## H.nchrs.log FALSE FALSE FALSE FALSE NA
## S.report.log FALSE TRUE FALSE FALSE NA
## A.said.log FALSE TRUE TRUE FALSE NA
## S.newyork.log FALSE FALSE FALSE FALSE NA
## H.npnct12.log FALSE FALSE TRUE FALSE NA
## H.npnct04.log FALSE TRUE FALSE FALSE NA
## PubDate.last10.log FALSE FALSE FALSE FALSE NA
## H.ndgts.log FALSE FALSE FALSE FALSE NA
## H.nwrds.log FALSE FALSE FALSE FALSE NA
## H.npnct17.log FALSE TRUE FALSE FALSE NA
## S.npnct04.log FALSE TRUE FALSE FALSE NA
## PubDate.second.fctr FALSE FALSE TRUE FALSE NA
## S.can.log FALSE TRUE FALSE FALSE NA
## S.share.log FALSE TRUE FALSE FALSE NA
## H.new.log FALSE TRUE FALSE FALSE NA
## S.will.log FALSE FALSE FALSE FALSE NA
## H.npnct07.log FALSE FALSE TRUE FALSE NA
## S.take.log FALSE TRUE FALSE FALSE NA
## PubDate.minute.fctr FALSE FALSE FALSE FALSE NA
## PubDate.last1.log FALSE FALSE FALSE FALSE NA
## S.npnct01.log FALSE TRUE FALSE FALSE NA
## H.npnct13.log FALSE FALSE TRUE FALSE NA
## H.day.log FALSE TRUE FALSE FALSE NA
## H.npnct14.log FALSE TRUE FALSE FALSE NA
## S.npnct12.log FALSE FALSE FALSE FALSE NA
## S.compani.log FALSE FALSE FALSE FALSE NA
## H.npnct01.log FALSE TRUE FALSE FALSE NA
## A.nwrds.unq.log FALSE FALSE FALSE FALSE NA
## S.show.log FALSE TRUE FALSE FALSE NA
## PubDate.hour.fctr FALSE FALSE FALSE FALSE NA
## S.presid.log FALSE TRUE FALSE FALSE NA
## S.npnct16.log FALSE FALSE FALSE FALSE NA
## S.make.log FALSE TRUE FALSE FALSE NA
## S.state.log FALSE TRUE TRUE FALSE NA
## A.state.log FALSE TRUE TRUE FALSE NA
## A.npnct27.log FALSE TRUE TRUE FALSE NA
## S.year.log FALSE FALSE FALSE FALSE NA
## H.npnct16.log FALSE FALSE FALSE FALSE NA
## S.intern.log FALSE TRUE FALSE FALSE NA
## H.has.ebola FALSE TRUE FALSE FALSE NA
## PubDate.date.fctr FALSE FALSE TRUE FALSE NA
## H.report.log FALSE TRUE FALSE FALSE NA
## H.X2014.log FALSE TRUE FALSE FALSE NA
## S.npnct13.log FALSE FALSE FALSE FALSE NA
## H.week.log FALSE TRUE FALSE FALSE NA
## S.time.log FALSE FALSE FALSE FALSE NA
## PubDate.wkend FALSE FALSE FALSE FALSE NA
## S.week.log FALSE FALSE FALSE FALSE NA
## A.nwrds.log FALSE FALSE FALSE FALSE NA
## A.nchrs.log FALSE FALSE FALSE FALSE NA
## PubDate.last100.log FALSE FALSE TRUE FALSE NA
## S.day.log FALSE TRUE FALSE FALSE NA
## A.npnct17.log FALSE TRUE TRUE FALSE NA
## S.first.log FALSE TRUE FALSE FALSE NA
## H.newyork.log FALSE TRUE FALSE FALSE NA
## S.articl.log FALSE TRUE FALSE FALSE NA
## H.daili.log FALSE TRUE FALSE FALSE NA
## H.npnct30.log FALSE TRUE FALSE FALSE NA
## .rnorm FALSE FALSE FALSE FALSE NA
## S.new.log FALSE FALSE FALSE FALSE NA
## S.npnct06.log FALSE TRUE FALSE FALSE NA
## S.fashion.log FALSE TRUE FALSE FALSE NA
## H.npnct15.log FALSE TRUE FALSE FALSE NA
## S.npnct30.log FALSE TRUE FALSE FALSE NA
## A.has.year.colon FALSE TRUE TRUE FALSE NA
## H.npnct02.log FALSE TRUE FALSE FALSE NA
## S.npnct22.log FALSE TRUE FALSE FALSE NA
## S.npnct03.log FALSE TRUE TRUE FALSE NA
## H.npnct05.log FALSE TRUE TRUE FALSE NA
## A.npnct07.log FALSE TRUE TRUE FALSE NA
## S.npnct02.log FALSE TRUE TRUE FALSE NA
## S.npnct08.log FALSE TRUE TRUE FALSE NA
## S.npnct09.log FALSE TRUE TRUE FALSE NA
## A.one.log FALSE TRUE TRUE FALSE NA
## S.one.log FALSE TRUE TRUE FALSE NA
## H.npnct11.log FALSE TRUE TRUE FALSE NA
## S.npnct15.log FALSE TRUE FALSE FALSE NA
## A.npnct15.log FALSE TRUE FALSE FALSE NA
## A.npnct18.log FALSE TRUE TRUE FALSE NA
## H.npnct22.log FALSE TRUE TRUE FALSE NA
## A.npnct19.log FALSE TRUE TRUE FALSE NA
## A.articl.log FALSE TRUE FALSE FALSE NA
## A.can.log FALSE TRUE FALSE FALSE NA
## A.compani.log FALSE FALSE FALSE FALSE NA
## A.day.log FALSE TRUE FALSE FALSE NA
## A.fashion.log FALSE TRUE FALSE FALSE NA
## A.first.log FALSE TRUE FALSE FALSE NA
## A.has.http FALSE TRUE TRUE FALSE NA
## A.intern.log FALSE TRUE FALSE FALSE NA
## A.make.log FALSE TRUE FALSE FALSE NA
## A.ndgts.log FALSE FALSE FALSE FALSE NA
## A.new.log FALSE FALSE FALSE FALSE NA
## A.newyork.log FALSE FALSE FALSE FALSE NA
## A.npnct01.log FALSE TRUE FALSE FALSE NA
## A.npnct02.log FALSE TRUE TRUE FALSE NA
## A.npnct03.log FALSE TRUE TRUE FALSE NA
## A.npnct04.log FALSE TRUE FALSE FALSE NA
## A.npnct05.log TRUE TRUE NA FALSE NA
## A.npnct06.log FALSE TRUE FALSE FALSE NA
## A.npnct08.log FALSE TRUE TRUE FALSE NA
## A.npnct09.log FALSE TRUE TRUE FALSE NA
## A.npnct10.log TRUE TRUE NA FALSE NA
## A.npnct11.log FALSE TRUE TRUE FALSE NA
## A.npnct12.log FALSE FALSE FALSE FALSE NA
## A.npnct13.log FALSE FALSE FALSE FALSE NA
## A.npnct16.log FALSE FALSE FALSE FALSE NA
## A.npnct20.log FALSE TRUE TRUE FALSE NA
## A.npnct22.log FALSE TRUE FALSE FALSE NA
## A.npnct24.log TRUE TRUE NA FALSE NA
## A.npnct25.log FALSE TRUE TRUE FALSE NA
## A.npnct26.log TRUE TRUE TRUE FALSE NA
## A.npnct28.log TRUE TRUE NA FALSE NA
## A.npnct29.log TRUE TRUE NA FALSE NA
## A.npnct30.log FALSE TRUE FALSE FALSE NA
## A.npnct31.log TRUE TRUE NA FALSE NA
## A.npnct32.log TRUE TRUE NA FALSE NA
## A.nuppr.log FALSE FALSE FALSE FALSE NA
## A.presid.log FALSE TRUE FALSE FALSE NA
## A.report.log FALSE TRUE FALSE FALSE NA
## A.share.log FALSE TRUE FALSE FALSE NA
## A.show.log FALSE TRUE FALSE FALSE NA
## A.take.log FALSE TRUE FALSE FALSE NA
## A.time.log FALSE FALSE FALSE FALSE NA
## A.week.log FALSE FALSE FALSE FALSE NA
## A.will.log FALSE FALSE FALSE FALSE NA
## A.year.log FALSE FALSE FALSE FALSE NA
## H.fashion.log FALSE TRUE FALSE FALSE NA
## H.has.http TRUE TRUE NA FALSE NA
## H.has.year.colon FALSE TRUE FALSE FALSE NA
## H.npnct03.log FALSE TRUE TRUE FALSE NA
## H.npnct06.log FALSE TRUE FALSE FALSE NA
## H.npnct08.log FALSE TRUE FALSE FALSE NA
## H.npnct10.log TRUE TRUE NA FALSE NA
## H.npnct18.log TRUE TRUE NA FALSE NA
## H.npnct19.log TRUE TRUE NA FALSE NA
## H.npnct20.log TRUE TRUE NA FALSE NA
## H.npnct23.log TRUE TRUE NA FALSE NA
## H.npnct24.log TRUE TRUE NA FALSE NA
## H.npnct25.log TRUE TRUE NA FALSE NA
## H.npnct26.log TRUE TRUE TRUE FALSE NA
## H.npnct27.log TRUE TRUE NA FALSE NA
## H.npnct28.log TRUE TRUE NA FALSE NA
## H.npnct29.log TRUE TRUE NA FALSE NA
## H.npnct31.log TRUE TRUE NA FALSE NA
## H.npnct32.log TRUE TRUE NA FALSE NA
## H.nwrds.unq.log FALSE FALSE FALSE FALSE NA
## H.X2015.log FALSE TRUE FALSE FALSE NA
## Popular FALSE FALSE FALSE TRUE NA
## Popular.fctr NA NA NA NA NA
## PubDate.last1 FALSE FALSE FALSE FALSE NA
## PubDate.last10 FALSE FALSE FALSE FALSE NA
## PubDate.last100 FALSE FALSE FALSE FALSE NA
## PubDate.month.fctr FALSE FALSE FALSE FALSE NA
## PubDate.POSIX FALSE FALSE TRUE FALSE NA
## PubDate.year.fctr TRUE TRUE NA FALSE NA
## PubDate.zoo FALSE FALSE TRUE FALSE NA
## S.has.http TRUE TRUE NA FALSE NA
## S.has.year.colon FALSE TRUE TRUE FALSE NA
## S.nchrs.log FALSE FALSE FALSE FALSE NA
## S.npnct05.log TRUE TRUE NA FALSE NA
## S.npnct07.log FALSE TRUE TRUE FALSE NA
## S.npnct10.log TRUE TRUE NA FALSE NA
## S.npnct11.log FALSE TRUE TRUE FALSE NA
## S.npnct14.log FALSE FALSE FALSE FALSE NA
## S.npnct17.log FALSE TRUE TRUE FALSE NA
## S.npnct18.log TRUE TRUE NA FALSE NA
## S.npnct19.log TRUE TRUE NA FALSE NA
## S.npnct20.log TRUE TRUE NA FALSE NA
## S.npnct21.log FALSE FALSE FALSE FALSE NA
## S.npnct23.log FALSE TRUE FALSE FALSE NA
## S.npnct24.log TRUE TRUE NA FALSE NA
## S.npnct25.log FALSE TRUE FALSE FALSE NA
## S.npnct26.log TRUE TRUE TRUE FALSE NA
## S.npnct27.log TRUE TRUE NA FALSE NA
## S.npnct28.log TRUE TRUE NA FALSE NA
## S.npnct29.log TRUE TRUE NA FALSE NA
## S.npnct31.log TRUE TRUE NA FALSE NA
## S.npnct32.log TRUE TRUE NA FALSE NA
## S.nwrds.log FALSE FALSE FALSE FALSE NA
## S.nwrds.unq.log FALSE FALSE FALSE FALSE NA
## S.said.log FALSE TRUE TRUE FALSE NA
## UniqueID FALSE FALSE TRUE FALSE TRUE
## WordCount FALSE FALSE FALSE FALSE NA
## rsp_var importance Low.cor.X.glm.importance
## A.npnct23.log NA 1.000000e+02 1.000000e+02
## WordCount.log NA 1.288801e-05 1.288801e-05
## myCategory.fctr NA 1.002204e-05 1.002204e-05
## H.npnct21.log NA 5.018143e-06 5.018143e-06
## A.npnct21.log NA 4.625851e-06 4.625851e-06
## S.nuppr.log NA 4.569486e-06 4.569486e-06
## A.npnct14.log NA 3.906452e-06 3.906452e-06
## H.today.log NA 3.716137e-06 3.716137e-06
## H.nuppr.log NA 3.182025e-06 3.182025e-06
## H.npnct09.log NA 3.059125e-06 3.059125e-06
## PubDate.wkday.fctr NA 2.985990e-06 2.985990e-06
## S.ndgts.log NA 2.333324e-06 2.333324e-06
## H.nchrs.log NA 2.327769e-06 2.327769e-06
## S.report.log NA 2.241758e-06 2.241758e-06
## A.said.log NA 2.225691e-06 2.225691e-06
## S.newyork.log NA 2.215953e-06 2.215953e-06
## H.npnct12.log NA 2.149261e-06 2.149261e-06
## H.npnct04.log NA 2.100497e-06 2.100497e-06
## PubDate.last10.log NA 1.997887e-06 1.997887e-06
## H.ndgts.log NA 1.993583e-06 1.993583e-06
## H.nwrds.log NA 1.855196e-06 1.855196e-06
## H.npnct17.log NA 1.847313e-06 1.847313e-06
## S.npnct04.log NA 1.760068e-06 1.760068e-06
## PubDate.second.fctr NA 1.752346e-06 1.752346e-06
## S.can.log NA 1.698109e-06 1.698109e-06
## S.share.log NA 1.577347e-06 1.577347e-06
## H.new.log NA 1.501228e-06 1.501228e-06
## S.will.log NA 1.438120e-06 1.438120e-06
## H.npnct07.log NA 1.251748e-06 1.251748e-06
## S.take.log NA 1.242853e-06 1.242853e-06
## PubDate.minute.fctr NA 1.202598e-06 1.202598e-06
## PubDate.last1.log NA 1.200389e-06 1.200389e-06
## S.npnct01.log NA 1.175535e-06 1.175535e-06
## H.npnct13.log NA 1.166146e-06 1.166146e-06
## H.day.log NA 1.165449e-06 1.165449e-06
## H.npnct14.log NA 1.162212e-06 1.162212e-06
## S.npnct12.log NA 1.145878e-06 1.145878e-06
## S.compani.log NA 1.136156e-06 1.136156e-06
## H.npnct01.log NA 1.095863e-06 1.095863e-06
## A.nwrds.unq.log NA 1.074022e-06 1.074022e-06
## S.show.log NA 1.071063e-06 1.071063e-06
## PubDate.hour.fctr NA 1.052433e-06 1.052433e-06
## S.presid.log NA 1.031221e-06 1.031221e-06
## S.npnct16.log NA 1.022348e-06 1.022348e-06
## S.make.log NA 1.008816e-06 1.008816e-06
## S.state.log NA 9.110520e-07 9.110520e-07
## A.state.log NA 9.110520e-07 9.110520e-07
## A.npnct27.log NA 9.110520e-07 9.110520e-07
## S.year.log NA 8.851511e-07 8.851511e-07
## H.npnct16.log NA 8.710662e-07 8.710662e-07
## S.intern.log NA 8.387696e-07 8.387696e-07
## H.has.ebola NA 8.241580e-07 8.241580e-07
## PubDate.date.fctr NA 8.233073e-07 8.233073e-07
## H.report.log NA 8.039331e-07 8.039331e-07
## H.X2014.log NA 7.740347e-07 7.740347e-07
## S.npnct13.log NA 7.695263e-07 7.695263e-07
## H.week.log NA 7.110312e-07 7.110312e-07
## S.time.log NA 6.384092e-07 6.384092e-07
## PubDate.wkend NA 6.089193e-07 6.089193e-07
## S.week.log NA 5.723540e-07 5.723540e-07
## A.nwrds.log NA 5.580949e-07 5.580949e-07
## A.nchrs.log NA 5.287682e-07 5.287682e-07
## PubDate.last100.log NA 5.212444e-07 5.212444e-07
## S.day.log NA 3.145965e-07 3.145965e-07
## A.npnct17.log NA 3.000151e-07 3.000151e-07
## S.first.log NA 2.555518e-07 2.555518e-07
## H.newyork.log NA 1.800829e-07 1.800829e-07
## S.articl.log NA 1.796068e-07 1.796068e-07
## H.daili.log NA 1.287108e-07 1.287108e-07
## H.npnct30.log NA 9.686374e-08 9.686374e-08
## .rnorm NA 8.784761e-08 8.784761e-08
## S.new.log NA 4.862671e-08 4.862671e-08
## S.npnct06.log NA 2.225651e-08 2.225651e-08
## S.fashion.log NA 1.098049e-09 1.098049e-09
## H.npnct15.log NA 7.395235e-10 7.395235e-10
## S.npnct30.log NA 5.609428e-10 5.609428e-10
## A.has.year.colon NA 2.912418e-10 2.912418e-10
## H.npnct02.log NA 2.666142e-10 2.666142e-10
## S.npnct22.log NA 2.461340e-10 2.461340e-10
## S.npnct03.log NA 2.304835e-10 2.304835e-10
## H.npnct05.log NA 1.846671e-10 1.846671e-10
## A.npnct07.log NA 1.653979e-10 1.653979e-10
## S.npnct02.log NA 7.575296e-11 7.575296e-11
## S.npnct08.log NA 7.353677e-11 7.353677e-11
## S.npnct09.log NA 6.715377e-11 6.715377e-11
## A.one.log NA 6.660788e-11 6.660788e-11
## S.one.log NA 6.580549e-11 6.580549e-11
## H.npnct11.log NA 6.416570e-11 6.416570e-11
## S.npnct15.log NA 5.392046e-12 5.392046e-12
## A.npnct15.log NA 4.905261e-12 4.905261e-12
## A.npnct18.log NA 1.783598e-12 1.783598e-12
## H.npnct22.log NA 1.467473e-12 1.467473e-12
## A.npnct19.log NA 0.000000e+00 0.000000e+00
## A.articl.log NA NA NA
## A.can.log NA NA NA
## A.compani.log NA NA NA
## A.day.log NA NA NA
## A.fashion.log NA NA NA
## A.first.log NA NA NA
## A.has.http NA NA NA
## A.intern.log NA NA NA
## A.make.log NA NA NA
## A.ndgts.log NA NA NA
## A.new.log NA NA NA
## A.newyork.log NA NA NA
## A.npnct01.log NA NA NA
## A.npnct02.log NA NA NA
## A.npnct03.log NA NA NA
## A.npnct04.log NA NA NA
## A.npnct05.log NA NA NA
## A.npnct06.log NA NA NA
## A.npnct08.log NA NA NA
## A.npnct09.log NA NA NA
## A.npnct10.log NA NA NA
## A.npnct11.log NA NA NA
## A.npnct12.log NA NA NA
## A.npnct13.log NA NA NA
## A.npnct16.log NA NA NA
## A.npnct20.log NA NA NA
## A.npnct22.log NA NA NA
## A.npnct24.log NA NA NA
## A.npnct25.log NA NA NA
## A.npnct26.log NA NA NA
## A.npnct28.log NA NA NA
## A.npnct29.log NA NA NA
## A.npnct30.log NA NA NA
## A.npnct31.log NA NA NA
## A.npnct32.log NA NA NA
## A.nuppr.log NA NA NA
## A.presid.log NA NA NA
## A.report.log NA NA NA
## A.share.log NA NA NA
## A.show.log NA NA NA
## A.take.log NA NA NA
## A.time.log NA NA NA
## A.week.log NA NA NA
## A.will.log NA NA NA
## A.year.log NA NA NA
## H.fashion.log NA NA NA
## H.has.http NA NA NA
## H.has.year.colon NA NA NA
## H.npnct03.log NA NA NA
## H.npnct06.log NA NA NA
## H.npnct08.log NA NA NA
## H.npnct10.log NA NA NA
## H.npnct18.log NA NA NA
## H.npnct19.log NA NA NA
## H.npnct20.log NA NA NA
## H.npnct23.log NA NA NA
## H.npnct24.log NA NA NA
## H.npnct25.log NA NA NA
## H.npnct26.log NA NA NA
## H.npnct27.log NA NA NA
## H.npnct28.log NA NA NA
## H.npnct29.log NA NA NA
## H.npnct31.log NA NA NA
## H.npnct32.log NA NA NA
## H.nwrds.unq.log NA NA NA
## H.X2015.log NA NA NA
## Popular NA NA NA
## Popular.fctr TRUE NA NA
## PubDate.last1 NA NA NA
## PubDate.last10 NA NA NA
## PubDate.last100 NA NA NA
## PubDate.month.fctr NA NA NA
## PubDate.POSIX NA NA NA
## PubDate.year.fctr NA NA NA
## PubDate.zoo NA NA NA
## S.has.http NA NA NA
## S.has.year.colon NA NA NA
## S.nchrs.log NA NA NA
## S.npnct05.log NA NA NA
## S.npnct07.log NA NA NA
## S.npnct10.log NA NA NA
## S.npnct11.log NA NA NA
## S.npnct14.log NA NA NA
## S.npnct17.log NA NA NA
## S.npnct18.log NA NA NA
## S.npnct19.log NA NA NA
## S.npnct20.log NA NA NA
## S.npnct21.log NA NA NA
## S.npnct23.log NA NA NA
## S.npnct24.log NA NA NA
## S.npnct25.log NA NA NA
## S.npnct26.log NA NA NA
## S.npnct27.log NA NA NA
## S.npnct28.log NA NA NA
## S.npnct29.log NA NA NA
## S.npnct31.log NA NA NA
## S.npnct32.log NA NA NA
## S.nwrds.log NA NA NA
## S.nwrds.unq.log NA NA NA
## S.said.log NA NA NA
## UniqueID NA NA NA
## WordCount NA NA NA# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (length(vars <- subset(glb_feats_df, importance > 0)$id) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", length(vars))
vars <- vars[1:5]
}
require(reshape2)
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in vars) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
# plot_vars_df <- subset(glb_feats_df, importance >
# glb_feats_df[glb_feats_df$id == ".rnorm", "importance"])
plot_vars_df <- orderBy(~ -importance, glb_feats_df)
if (nrow(plot_vars_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(plot_vars_df) > 1, plot_vars_df$id[2],
".rownames"),
feat_y=plot_vars_df$id[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_vars)
# + facet_wrap(reformulate(plot_vars_df$id[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(plot_vars_df <- subset(glb_feats_df, importance > 0)) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(plot_vars_df) > 1, plot_vars_df$id[2],
".rownames"),
feat_y=plot_vars_df$id[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_vars,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
glb_analytics_diag_plots(obs_df=glb_OOBent_df, mdl_id=glb_sel_mdl_id,
prob_threshold=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_analytics_diag_plots(obs_df = glb_OOBent_df, mdl_id =
## glb_sel_mdl_id, : Limiting important feature scatter plots to 5 out of 92
## [1] "Min/Max Boundaries: "
## UniqueID Popular.fctr Popular.fctr.predict.Low.cor.X.glm.prob
## 1132 1132 Y 2.220446e-16
## 172 172 Y 2.803341e-03
## 163 163 Y 7.997086e-02
## 37 37 Y 1.759437e-01
## 104 104 Y 2.868026e-01
## 4 4 Y 2.948342e-01
## 31 31 N 1.801392e-03
## 6018 6018 N 3.033259e-04
## 6370 6370 Y 4.834288e-01
## 17 17 N 8.939842e-01
## Popular.fctr.predict.Low.cor.X.glm
## 1132 N
## 172 N
## 163 N
## 37 N
## 104 N
## 4 N
## 31 N
## 6018 N
## 6370 Y
## 17 Y
## Popular.fctr.predict.Low.cor.X.glm.accurate
## 1132 FALSE
## 172 FALSE
## 163 FALSE
## 37 FALSE
## 104 FALSE
## 4 FALSE
## 31 TRUE
## 6018 TRUE
## 6370 TRUE
## 17 FALSE
## Popular.fctr.predict.Low.cor.X.glm.error .label
## 1132 -0.30000000 1132
## 172 -0.29719666 172
## 163 -0.22002914 163
## 37 -0.12405630 37
## 104 -0.01319737 104
## 4 -0.00516582 4
## 31 0.00000000 31
## 6018 0.00000000 6018
## 6370 0.00000000 6370
## 17 0.59398421 17
## [1] "Inaccurate: "
## UniqueID Popular.fctr Popular.fctr.predict.Low.cor.X.glm.prob
## 1132 1132 Y 2.220446e-16
## 5486 5486 Y 2.977582e-12
## 1403 1403 Y 5.839170e-12
## 6387 6387 Y 1.001307e-09
## 1273 1273 Y 1.503972e-03
## 172 172 Y 2.803341e-03
## Popular.fctr.predict.Low.cor.X.glm
## 1132 N
## 5486 N
## 1403 N
## 6387 N
## 1273 N
## 172 N
## Popular.fctr.predict.Low.cor.X.glm.accurate
## 1132 FALSE
## 5486 FALSE
## 1403 FALSE
## 6387 FALSE
## 1273 FALSE
## 172 FALSE
## Popular.fctr.predict.Low.cor.X.glm.error
## 1132 -0.3000000
## 5486 -0.3000000
## 1403 -0.3000000
## 6387 -0.3000000
## 1273 -0.2984960
## 172 -0.2971967
## UniqueID Popular.fctr Popular.fctr.predict.Low.cor.X.glm.prob
## 5573 5573 Y 0.03047345
## 4632 4632 Y 0.11827833
## 3557 3557 N 0.54259052
## 2412 2412 N 0.68022957
## 664 664 N 0.69597987
## 6435 6435 N 0.86263285
## Popular.fctr.predict.Low.cor.X.glm
## 5573 N
## 4632 N
## 3557 Y
## 2412 Y
## 664 Y
## 6435 Y
## Popular.fctr.predict.Low.cor.X.glm.accurate
## 5573 FALSE
## 4632 FALSE
## 3557 FALSE
## 2412 FALSE
## 664 FALSE
## 6435 FALSE
## Popular.fctr.predict.Low.cor.X.glm.error
## 5573 -0.2695265
## 4632 -0.1817217
## 3557 0.2425905
## 2412 0.3802296
## 664 0.3959799
## 6435 0.5626329
## UniqueID Popular.fctr Popular.fctr.predict.Low.cor.X.glm.prob
## 1667 1667 N 0.9396353
## 3258 3258 N 0.9504278
## 4975 4975 N 0.9510598
## 4771 4771 N 0.9516763
## 770 770 N 0.9785031
## 4882 4882 N 0.9800978
## Popular.fctr.predict.Low.cor.X.glm
## 1667 Y
## 3258 Y
## 4975 Y
## 4771 Y
## 770 Y
## 4882 Y
## Popular.fctr.predict.Low.cor.X.glm.accurate
## 1667 FALSE
## 3258 FALSE
## 4975 FALSE
## 4771 FALSE
## 770 FALSE
## 4882 FALSE
## Popular.fctr.predict.Low.cor.X.glm.error
## 1667 0.6396353
## 3258 0.6504278
## 4975 0.6510598
## 4771 0.6516763
## 770 0.6785031
## 4882 0.6800978
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBent_df <- glb_get_predictions(df=glb_OOBent_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBent_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBent_df[, glb_rsp_var])$table))
FN_OOB_ids <- c(4721, 4020, 693, 92)
print(glb_OOBent_df[glb_OOBent_df$UniqueID %in% FN_OOB_ids,
grep(glb_rsp_var, names(glb_OOBent_df), value=TRUE)])## [1] Popular.fctr
## [2] Popular.fctr.predict.Low.cor.X.glm.prob
## [3] Popular.fctr.predict.Low.cor.X.glm
## [4] Popular.fctr.predict.Low.cor.X.glm.accurate
## <0 rows> (or 0-length row.names)print(glb_OOBent_df[glb_OOBent_df$UniqueID %in% FN_OOB_ids,
glb_feats_df$id[1:5]])## [1] A.npnct23.log WordCount.log myCategory.fctr H.npnct21.log
## [5] A.npnct21.log
## <0 rows> (or 0-length row.names)print(glb_OOBent_df[glb_OOBent_df$UniqueID %in% FN_OOB_ids,
glb_txt_vars])## [1] Headline Snippet Abstract
## <0 rows> (or 0-length row.names)write.csv(glb_OOBent_df[, c("UniqueID",
grep(glb_rsp_var, names(glb_OOBent_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBent.csv"), row.names=FALSE)
# print(glb_entity_df[glb_entity_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_entity_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_entity_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_entity_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_entity_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_entity_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_entity_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 6 2 544.291 561.516 17.226
## 12 fit.models 6 3 561.517 NA NAsav_entity_df <- glb_entity_df
print(setdiff(names(glb_trnent_df), names(glb_entity_df)))## character(0)print(setdiff(names(glb_fitent_df), names(glb_entity_df)))## character(0)print(setdiff(names(glb_OOBent_df), names(glb_entity_df)))## [1] "Popular.fctr.predict.Low.cor.X.glm.prob"
## [2] "Popular.fctr.predict.Low.cor.X.glm"
## [3] "Popular.fctr.predict.Low.cor.X.glm.accurate"for (col in setdiff(names(glb_OOBent_df), names(glb_entity_df)))
# Merge or cbind ?
glb_entity_df[glb_entity_df$.lcn == "OOB", col] <- glb_OOBent_df[, col]
print(setdiff(names(glb_newent_df), names(glb_entity_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_entity_df, #glb_trnent_df, glb_fitent_df, glb_OOBent_df, glb_newent_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 12 fit.models 6 3 561.517 569.037 7.52
## 13 fit.data.training 7 0 569.038 NA NAStep 7.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
entity_df=glb_fitent_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=mdl_feats_df$id, model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## id importance
## A.npnct23.log A.npnct23.log 1.000000e+02
## WordCount.log WordCount.log 1.288801e-05
## myCategory.fctr myCategory.fctr 1.002204e-05
## H.npnct21.log H.npnct21.log 5.018143e-06
## A.npnct21.log A.npnct21.log 4.625851e-06
## S.nuppr.log S.nuppr.log 4.569486e-06
## A.npnct14.log A.npnct14.log 3.906452e-06
## H.today.log H.today.log 3.716137e-06
## H.nuppr.log H.nuppr.log 3.182025e-06
## H.npnct09.log H.npnct09.log 3.059125e-06
## PubDate.wkday.fctr PubDate.wkday.fctr 2.985990e-06
## S.ndgts.log S.ndgts.log 2.333324e-06
## H.nchrs.log H.nchrs.log 2.327769e-06
## S.report.log S.report.log 2.241758e-06
## A.said.log A.said.log 2.225691e-06
## S.newyork.log S.newyork.log 2.215953e-06
## H.npnct12.log H.npnct12.log 2.149261e-06
## H.npnct04.log H.npnct04.log 2.100497e-06
## PubDate.last10.log PubDate.last10.log 1.997887e-06
## H.ndgts.log H.ndgts.log 1.993583e-06
## H.nwrds.log H.nwrds.log 1.855196e-06
## H.npnct17.log H.npnct17.log 1.847313e-06
## S.npnct04.log S.npnct04.log 1.760068e-06
## PubDate.second.fctr PubDate.second.fctr 1.752346e-06
## S.can.log S.can.log 1.698109e-06
## S.share.log S.share.log 1.577347e-06
## H.new.log H.new.log 1.501228e-06
## S.will.log S.will.log 1.438120e-06
## H.npnct07.log H.npnct07.log 1.251748e-06
## S.take.log S.take.log 1.242853e-06
## PubDate.minute.fctr PubDate.minute.fctr 1.202598e-06
## PubDate.last1.log PubDate.last1.log 1.200389e-06
## S.npnct01.log S.npnct01.log 1.175535e-06
## H.npnct13.log H.npnct13.log 1.166146e-06
## H.day.log H.day.log 1.165449e-06
## H.npnct14.log H.npnct14.log 1.162212e-06
## S.npnct12.log S.npnct12.log 1.145878e-06
## S.compani.log S.compani.log 1.136156e-06
## H.npnct01.log H.npnct01.log 1.095863e-06
## A.nwrds.unq.log A.nwrds.unq.log 1.074022e-06
## S.show.log S.show.log 1.071063e-06
## PubDate.hour.fctr PubDate.hour.fctr 1.052433e-06
## S.presid.log S.presid.log 1.031221e-06
## S.npnct16.log S.npnct16.log 1.022348e-06
## S.make.log S.make.log 1.008816e-06
## S.state.log S.state.log 9.110520e-07
## A.state.log A.state.log 9.110520e-07
## A.npnct27.log A.npnct27.log 9.110520e-07
## S.year.log S.year.log 8.851511e-07
## H.npnct16.log H.npnct16.log 8.710662e-07
## S.intern.log S.intern.log 8.387696e-07
## H.has.ebola H.has.ebola 8.241580e-07
## PubDate.date.fctr PubDate.date.fctr 8.233073e-07
## H.report.log H.report.log 8.039331e-07
## H.X2014.log H.X2014.log 7.740347e-07
## S.npnct13.log S.npnct13.log 7.695263e-07
## H.week.log H.week.log 7.110312e-07
## S.time.log S.time.log 6.384092e-07
## PubDate.wkend PubDate.wkend 6.089193e-07
## S.week.log S.week.log 5.723540e-07
## A.nwrds.log A.nwrds.log 5.580949e-07
## A.nchrs.log A.nchrs.log 5.287682e-07
## PubDate.last100.log PubDate.last100.log 5.212444e-07
## S.day.log S.day.log 3.145965e-07
## A.npnct17.log A.npnct17.log 3.000151e-07
## S.first.log S.first.log 2.555518e-07
## H.newyork.log H.newyork.log 1.800829e-07
## S.articl.log S.articl.log 1.796068e-07
## H.daili.log H.daili.log 1.287108e-07
## H.npnct30.log H.npnct30.log 9.686374e-08
## .rnorm .rnorm 8.784761e-08
## S.new.log S.new.log 4.862671e-08
## S.npnct06.log S.npnct06.log 2.225651e-08
## S.fashion.log S.fashion.log 1.098049e-09
## H.npnct15.log H.npnct15.log 7.395235e-10
## S.npnct30.log S.npnct30.log 5.609428e-10
## A.has.year.colon A.has.year.colon 2.912418e-10
## H.npnct02.log H.npnct02.log 2.666142e-10
## S.npnct22.log S.npnct22.log 2.461340e-10
## S.npnct03.log S.npnct03.log 2.304835e-10
## H.npnct05.log H.npnct05.log 1.846671e-10
## A.npnct07.log A.npnct07.log 1.653979e-10
## S.npnct02.log S.npnct02.log 7.575296e-11
## S.npnct08.log S.npnct08.log 7.353677e-11
## S.npnct09.log S.npnct09.log 6.715377e-11
## A.one.log A.one.log 6.660788e-11
## S.one.log S.one.log 6.580549e-11
## H.npnct11.log H.npnct11.log 6.416570e-11
## S.npnct15.log S.npnct15.log 5.392046e-12
## A.npnct15.log A.npnct15.log 4.905261e-12
## A.npnct18.log A.npnct18.log 1.783598e-12
## H.npnct22.log H.npnct22.log 1.467473e-12
## A.npnct19.log A.npnct19.log 0.000000e+00
## [1] "fitting model: Final.glm"
## [1] " indep_vars: A.npnct23.log, WordCount.log, myCategory.fctr, H.npnct21.log, A.npnct21.log, S.nuppr.log, A.npnct14.log, H.today.log, H.nuppr.log, H.npnct09.log, PubDate.wkday.fctr, S.ndgts.log, H.nchrs.log, S.report.log, A.said.log, S.newyork.log, H.npnct12.log, H.npnct04.log, PubDate.last10.log, H.ndgts.log, H.nwrds.log, H.npnct17.log, S.npnct04.log, PubDate.second.fctr, S.can.log, S.share.log, H.new.log, S.will.log, H.npnct07.log, S.take.log, PubDate.minute.fctr, PubDate.last1.log, S.npnct01.log, H.npnct13.log, H.day.log, H.npnct14.log, S.npnct12.log, S.compani.log, H.npnct01.log, A.nwrds.unq.log, S.show.log, PubDate.hour.fctr, S.presid.log, S.npnct16.log, S.make.log, S.state.log, A.state.log, A.npnct27.log, S.year.log, H.npnct16.log, S.intern.log, H.has.ebola, PubDate.date.fctr, H.report.log, H.X2014.log, S.npnct13.log, H.week.log, S.time.log, PubDate.wkend, S.week.log, A.nwrds.log, A.nchrs.log, PubDate.last100.log, S.day.log, A.npnct17.log, S.first.log, H.newyork.log, S.articl.log, H.daili.log, H.npnct30.log, .rnorm, S.new.log, S.npnct06.log, S.fashion.log, H.npnct15.log, S.npnct30.log, A.has.year.colon, H.npnct02.log, S.npnct22.log, S.npnct03.log, H.npnct05.log, A.npnct07.log, S.npnct02.log, S.npnct08.log, S.npnct09.log, A.one.log, S.one.log, H.npnct11.log, S.npnct15.log, A.npnct15.log, A.npnct18.log, H.npnct22.log, A.npnct19.log"
## + Fold1: parameter=none## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1: parameter=none
## + Fold2: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2: parameter=none
## + Fold3: parameter=none## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 1651, 2876, 3675, 3676, 5318
## Warning: not plotting observations with leverage one:
## 1651, 2876, 3675, 3676, 5318
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.4904 -0.3514 -0.1591 0.0000 3.4579
##
## Coefficients:
## Estimate
## (Intercept) -4.468e+00
## A.npnct23.log 3.734e+00
## WordCount.log 1.114e+00
## `myCategory.fctrForeign#World#Asia Pacific` -4.609e+00
## `myCategory.fctr#Multimedia#` -4.741e+00
## `myCategory.fctrCulture#Arts#` -2.642e+00
## `myCategory.fctrBusiness#Business Day#Dealbook` -2.414e+00
## myCategory.fctrmyOther -4.504e+15
## `myCategory.fctrBusiness#Technology#` -1.870e+00
## `myCategory.fctrBusiness#Crosswords/Games#` 7.274e-01
## `myCategory.fctrTStyle##` -4.266e+00
## `myCategory.fctrForeign#World#` -1.912e+01
## `myCategory.fctrOpEd#Opinion#` 8.622e-01
## `myCategory.fctrStyles##Fashion` -5.306e+00
## `myCategory.fctr#Opinion#Room For Debate` -5.385e+00
## `myCategory.fctr#U.S.#Education` -2.209e+01
## `myCategory.fctr##` -2.658e+00
## `myCategory.fctrMetro#N.Y. / Region#` -1.622e+00
## `myCategory.fctrBusiness#Business Day#Small Business` -4.220e+00
## `myCategory.fctrStyles#U.S.#` -4.461e-01
## `myCategory.fctrTravel#Travel#` -4.282e+00
## `myCategory.fctr#Opinion#The Public Editor` 3.529e-01
## H.npnct21.log 1.446e+00
## A.npnct21.log 1.492e+00
## S.nuppr.log -6.385e-01
## A.npnct14.log 9.382e-01
## H.today.log -4.142e+00
## H.nuppr.log 9.798e-01
## H.npnct09.log 1.818e+00
## PubDate.wkday.fctr1 -4.263e-01
## PubDate.wkday.fctr2 -9.208e-01
## PubDate.wkday.fctr3 -6.235e-01
## PubDate.wkday.fctr4 -7.144e-01
## PubDate.wkday.fctr5 -7.790e-01
## PubDate.wkday.fctr6 -9.850e-01
## S.ndgts.log -2.848e-01
## H.nchrs.log -1.005e+00
## S.report.log -5.950e-01
## A.said.log 1.072e+00
## S.newyork.log 5.970e-01
## H.npnct12.log 3.582e-01
## H.npnct04.log -1.061e+00
## PubDate.last10.log 2.005e-01
## H.ndgts.log 2.901e-01
## H.nwrds.log -3.694e-01
## H.npnct17.log 5.765e-01
## S.npnct04.log -8.895e-01
## `PubDate.second.fctr(14.8,29.5]` -6.578e-02
## `PubDate.second.fctr(29.5,44.2]` -2.980e-02
## `PubDate.second.fctr(44.2,59.1]` -2.157e-01
## S.can.log -1.059e+00
## S.share.log -8.164e-01
## H.new.log -7.415e-01
## S.will.log -4.106e-01
## H.npnct07.log 5.531e-02
## S.take.log -2.510e-01
## `PubDate.minute.fctr(14.8,29.5]` -1.862e-02
## `PubDate.minute.fctr(29.5,44.2]` -2.274e-01
## `PubDate.minute.fctr(44.2,59.1]` 1.922e-02
## PubDate.last1.log -2.917e-02
## S.npnct01.log 2.084e+00
## H.npnct13.log 1.525e-01
## H.day.log -6.290e-01
## H.npnct14.log -1.090e-01
## S.npnct12.log -7.152e-02
## S.compani.log -4.443e-01
## H.npnct01.log -1.289e+00
## A.nwrds.unq.log 9.348e-03
## S.show.log -4.274e-01
## `PubDate.hour.fctr(7.67,15.3]` 1.093e-02
## `PubDate.hour.fctr(15.3,23]` 9.082e-02
## S.presid.log 7.588e-02
## S.npnct16.log -1.129e-01
## S.make.log -2.717e-01
## S.state.log -5.893e+13
## A.state.log 5.893e+13
## A.npnct27.log -5.893e+13
## S.year.log -6.081e-01
## H.npnct16.log 1.335e-02
## S.intern.log 5.583e-01
## H.has.ebola -5.383e-01
## `PubDate.date.fctr(7,13]` 9.452e-02
## `PubDate.date.fctr(13,19]` -1.172e-01
## `PubDate.date.fctr(19,25]` 6.087e-03
## `PubDate.date.fctr(25,31]` 1.119e-02
## H.report.log -1.545e-01
## H.X2014.log -1.087e+00
## S.npnct13.log -1.395e-01
## H.week.log -7.127e-01
## S.time.log -4.839e-01
## PubDate.wkend -1.033e-01
## S.week.log -2.814e-01
## A.nwrds.log -9.253e-01
## A.nchrs.log 4.484e-01
## PubDate.last100.log -3.426e-03
## S.day.log 1.413e-01
## A.npnct17.log 3.089e-01
## S.first.log -1.792e-01
## H.newyork.log -5.033e-02
## S.articl.log 6.588e-01
## H.daili.log 7.742e+03
## H.npnct30.log -1.549e-01
## .rnorm -7.433e-03
## S.new.log -1.483e-01
## S.npnct06.log 9.743e-01
## S.fashion.log -1.704e+00
## H.npnct15.log -2.986e+01
## S.npnct30.log -2.037e+01
## A.has.year.colon -2.008e+01
## H.npnct02.log -2.312e+01
## S.npnct22.log -3.362e+01
## S.npnct03.log -3.815e+01
## H.npnct05.log 3.176e-01
## A.npnct07.log -3.511e+01
## S.npnct02.log -2.448e+01
## S.npnct08.log 1.737e+01
## S.npnct09.log -1.687e+01
## A.one.log -3.117e+01
## S.one.log 3.114e+01
## H.npnct11.log -3.097e+01
## S.npnct15.log 2.715e+01
## A.npnct15.log -2.656e+01
## A.npnct18.log 1.332e+01
## H.npnct22.log -3.065e+01
## A.npnct19.log 1.983e+01
## Std. Error
## (Intercept) 1.668e+00
## A.npnct23.log 2.938e+00
## WordCount.log 7.292e-02
## `myCategory.fctrForeign#World#Asia Pacific` 6.315e-01
## `myCategory.fctr#Multimedia#` 7.702e-01
## `myCategory.fctrCulture#Arts#` 2.914e-01
## `myCategory.fctrBusiness#Business Day#Dealbook` 2.478e-01
## myCategory.fctrmyOther 1.089e+07
## `myCategory.fctrBusiness#Technology#` 2.634e-01
## `myCategory.fctrBusiness#Crosswords/Games#` 3.733e-01
## `myCategory.fctrTStyle##` 4.113e-01
## `myCategory.fctrForeign#World#` 8.880e+02
## `myCategory.fctrOpEd#Opinion#` 2.415e-01
## `myCategory.fctrStyles##Fashion` 1.095e+00
## `myCategory.fctr#Opinion#Room For Debate` 5.181e-01
## `myCategory.fctr#U.S.#Education` 1.045e+03
## `myCategory.fctr##` 2.309e-01
## `myCategory.fctrMetro#N.Y. / Region#` 3.996e-01
## `myCategory.fctrBusiness#Business Day#Small Business` 5.310e-01
## `myCategory.fctrStyles#U.S.#` 2.726e-01
## `myCategory.fctrTravel#Travel#` 1.028e+00
## `myCategory.fctr#Opinion#The Public Editor` 6.591e-01
## H.npnct21.log 2.565e-01
## A.npnct21.log 2.674e-01
## S.nuppr.log 1.314e-01
## A.npnct14.log 2.159e-01
## H.today.log 9.298e-01
## H.nuppr.log 3.389e-01
## H.npnct09.log 6.164e-01
## PubDate.wkday.fctr1 4.190e-01
## PubDate.wkday.fctr2 4.564e-01
## PubDate.wkday.fctr3 4.515e-01
## PubDate.wkday.fctr4 4.450e-01
## PubDate.wkday.fctr5 4.515e-01
## PubDate.wkday.fctr6 3.714e-01
## S.ndgts.log 1.230e-01
## H.nchrs.log 3.437e-01
## S.report.log 4.593e-01
## A.said.log 3.229e-01
## S.newyork.log 4.505e-01
## H.npnct12.log 1.700e-01
## H.npnct04.log 6.855e-01
## PubDate.last10.log 9.672e-02
## H.ndgts.log 2.059e-01
## H.nwrds.log 3.575e-01
## H.npnct17.log 4.579e-01
## S.npnct04.log 5.155e-01
## `PubDate.second.fctr(14.8,29.5]` 1.415e-01
## `PubDate.second.fctr(29.5,44.2]` 1.396e-01
## `PubDate.second.fctr(44.2,59.1]` 1.421e-01
## S.can.log 3.598e-01
## S.share.log 5.338e-01
## H.new.log 4.826e-01
## S.will.log 2.857e-01
## H.npnct07.log 1.533e-01
## S.take.log 4.399e-01
## `PubDate.minute.fctr(14.8,29.5]` 1.458e-01
## `PubDate.minute.fctr(29.5,44.2]` 1.418e-01
## `PubDate.minute.fctr(44.2,59.1]` 1.486e-01
## PubDate.last1.log 3.549e-02
## S.npnct01.log 1.095e+00
## H.npnct13.log 2.480e-01
## H.day.log 7.464e-01
## H.npnct14.log 1.614e-01
## S.npnct12.log 1.161e-01
## S.compani.log 3.278e-01
## H.npnct01.log 9.403e-01
## A.nwrds.unq.log 1.244e+00
## S.show.log 4.838e-01
## `PubDate.hour.fctr(7.67,15.3]` 1.957e-01
## `PubDate.hour.fctr(15.3,23]` 1.984e-01
## S.presid.log 4.181e-01
## S.npnct16.log 3.957e-01
## S.make.log 3.412e-01
## S.state.log 1.463e+14
## A.state.log 1.463e+14
## A.npnct27.log 1.463e+14
## S.year.log 3.760e-01
## H.npnct16.log 2.260e-01
## S.intern.log 7.281e-01
## H.has.ebola 3.766e-01
## `PubDate.date.fctr(7,13]` 1.575e-01
## `PubDate.date.fctr(13,19]` 1.570e-01
## `PubDate.date.fctr(19,25]` 1.534e-01
## `PubDate.date.fctr(25,31]` 1.665e-01
## H.report.log 7.273e-01
## H.X2014.log 1.227e+00
## S.npnct13.log 1.604e-01
## H.week.log 7.806e-01
## S.time.log 3.633e-01
## PubDate.wkend 3.539e-01
## S.week.log 3.983e-01
## A.nwrds.log 1.307e+00
## A.nchrs.log 4.131e-01
## PubDate.last100.log 3.624e-02
## S.day.log 4.829e-01
## A.npnct17.log 9.730e-01
## S.first.log 5.326e-01
## H.newyork.log 6.501e-01
## S.articl.log 8.675e-01
## H.daili.log 7.853e+06
## H.npnct30.log 1.591e+00
## .rnorm 5.054e-02
## S.new.log 2.596e-01
## S.npnct06.log 8.377e-01
## S.fashion.log 1.606e+00
## H.npnct15.log 3.770e+04
## S.npnct30.log 1.359e+04
## A.has.year.colon 9.858e+04
## H.npnct02.log 7.029e+04
## S.npnct22.log 1.365e+05
## S.npnct03.log 1.888e+05
## H.npnct05.log 1.607e+00
## A.npnct07.log 1.915e+05
## S.npnct02.log 3.058e+05
## S.npnct08.log 2.683e+05
## S.npnct09.log 2.683e+05
## A.one.log 3.731e+05
## S.one.log 3.731e+05
## H.npnct11.log 5.477e+05
## S.npnct15.log 5.207e+05
## A.npnct15.log 5.207e+05
## A.npnct18.log 3.301e+05
## H.npnct22.log 5.111e+05
## A.npnct19.log 8.129e+05
## z value Pr(>|z|)
## (Intercept) -2.679e+00 0.007374
## A.npnct23.log 1.271e+00 0.203821
## WordCount.log 1.528e+01 < 2e-16
## `myCategory.fctrForeign#World#Asia Pacific` -7.299e+00 2.91e-13
## `myCategory.fctr#Multimedia#` -6.156e+00 7.48e-10
## `myCategory.fctrCulture#Arts#` -9.068e+00 < 2e-16
## `myCategory.fctrBusiness#Business Day#Dealbook` -9.743e+00 < 2e-16
## myCategory.fctrmyOther -4.137e+08 < 2e-16
## `myCategory.fctrBusiness#Technology#` -7.098e+00 1.26e-12
## `myCategory.fctrBusiness#Crosswords/Games#` 1.949e+00 0.051330
## `myCategory.fctrTStyle##` -1.037e+01 < 2e-16
## `myCategory.fctrForeign#World#` -2.200e-02 0.982821
## `myCategory.fctrOpEd#Opinion#` 3.570e+00 0.000356
## `myCategory.fctrStyles##Fashion` -4.847e+00 1.25e-06
## `myCategory.fctr#Opinion#Room For Debate` -1.039e+01 < 2e-16
## `myCategory.fctr#U.S.#Education` -2.100e-02 0.983129
## `myCategory.fctr##` -1.151e+01 < 2e-16
## `myCategory.fctrMetro#N.Y. / Region#` -4.059e+00 4.93e-05
## `myCategory.fctrBusiness#Business Day#Small Business` -7.948e+00 1.89e-15
## `myCategory.fctrStyles#U.S.#` -1.636e+00 0.101756
## `myCategory.fctrTravel#Travel#` -4.166e+00 3.10e-05
## `myCategory.fctr#Opinion#The Public Editor` 5.350e-01 0.592342
## H.npnct21.log 5.636e+00 1.74e-08
## A.npnct21.log 5.580e+00 2.41e-08
## S.nuppr.log -4.859e+00 1.18e-06
## A.npnct14.log 4.346e+00 1.38e-05
## H.today.log -4.455e+00 8.40e-06
## H.nuppr.log 2.891e+00 0.003844
## H.npnct09.log 2.950e+00 0.003181
## PubDate.wkday.fctr1 -1.017e+00 0.308996
## PubDate.wkday.fctr2 -2.018e+00 0.043616
## PubDate.wkday.fctr3 -1.381e+00 0.167297
## PubDate.wkday.fctr4 -1.605e+00 0.108405
## PubDate.wkday.fctr5 -1.725e+00 0.084454
## PubDate.wkday.fctr6 -2.652e+00 0.008003
## S.ndgts.log -2.316e+00 0.020553
## H.nchrs.log -2.923e+00 0.003462
## S.report.log -1.295e+00 0.195242
## A.said.log 3.319e+00 0.000902
## S.newyork.log 1.325e+00 0.185161
## H.npnct12.log 2.107e+00 0.035149
## H.npnct04.log -1.548e+00 0.121562
## PubDate.last10.log 2.073e+00 0.038142
## H.ndgts.log 1.409e+00 0.158716
## H.nwrds.log -1.033e+00 0.301435
## H.npnct17.log 1.259e+00 0.208019
## S.npnct04.log -1.726e+00 0.084423
## `PubDate.second.fctr(14.8,29.5]` -4.650e-01 0.642118
## `PubDate.second.fctr(29.5,44.2]` -2.130e-01 0.830954
## `PubDate.second.fctr(44.2,59.1]` -1.518e+00 0.129105
## S.can.log -2.942e+00 0.003262
## S.share.log -1.529e+00 0.126169
## H.new.log -1.537e+00 0.124415
## S.will.log -1.437e+00 0.150643
## H.npnct07.log 3.610e-01 0.718266
## S.take.log -5.700e-01 0.568344
## `PubDate.minute.fctr(14.8,29.5]` -1.280e-01 0.898374
## `PubDate.minute.fctr(29.5,44.2]` -1.604e+00 0.108706
## `PubDate.minute.fctr(44.2,59.1]` 1.290e-01 0.897142
## PubDate.last1.log -8.220e-01 0.411016
## S.npnct01.log 1.904e+00 0.056911
## H.npnct13.log 6.150e-01 0.538580
## H.day.log -8.430e-01 0.399405
## H.npnct14.log -6.760e-01 0.499345
## S.npnct12.log -6.160e-01 0.537764
## S.compani.log -1.356e+00 0.175243
## H.npnct01.log -1.371e+00 0.170527
## A.nwrds.unq.log 8.000e-03 0.994005
## S.show.log -8.830e-01 0.377038
## `PubDate.hour.fctr(7.67,15.3]` 5.600e-02 0.955479
## `PubDate.hour.fctr(15.3,23]` 4.580e-01 0.647163
## S.presid.log 1.810e-01 0.855990
## S.npnct16.log -2.850e-01 0.775410
## S.make.log -7.960e-01 0.425854
## S.state.log -4.030e-01 0.687183
## A.state.log 4.030e-01 0.687183
## A.npnct27.log -4.030e-01 0.687183
## S.year.log -1.617e+00 0.105870
## H.npnct16.log 5.900e-02 0.952880
## S.intern.log 7.670e-01 0.443189
## H.has.ebola -1.429e+00 0.152934
## `PubDate.date.fctr(7,13]` 6.000e-01 0.548304
## `PubDate.date.fctr(13,19]` -7.470e-01 0.455265
## `PubDate.date.fctr(19,25]` 4.000e-02 0.968344
## `PubDate.date.fctr(25,31]` 6.700e-02 0.946419
## H.report.log -2.120e-01 0.831780
## H.X2014.log -8.860e-01 0.375872
## S.npnct13.log -8.700e-01 0.384386
## H.week.log -9.130e-01 0.361273
## S.time.log -1.332e+00 0.182944
## PubDate.wkend -2.920e-01 0.770474
## S.week.log -7.060e-01 0.479879
## A.nwrds.log -7.080e-01 0.478860
## A.nchrs.log 1.085e+00 0.277722
## PubDate.last100.log -9.500e-02 0.924695
## S.day.log 2.930e-01 0.769858
## A.npnct17.log 3.170e-01 0.750899
## S.first.log -3.360e-01 0.736546
## H.newyork.log -7.700e-02 0.938289
## S.articl.log 7.590e-01 0.447600
## H.daili.log 1.000e-03 0.999213
## H.npnct30.log -9.700e-02 0.922445
## .rnorm -1.470e-01 0.883063
## S.new.log -5.710e-01 0.567862
## S.npnct06.log 1.163e+00 0.244766
## S.fashion.log -1.061e+00 0.288582
## H.npnct15.log -1.000e-03 0.999368
## S.npnct30.log -1.000e-03 0.998804
## A.has.year.colon 0.000e+00 0.999837
## H.npnct02.log 0.000e+00 0.999738
## S.npnct22.log 0.000e+00 0.999804
## S.npnct03.log 0.000e+00 0.999839
## H.npnct05.log 1.980e-01 0.843365
## A.npnct07.log 0.000e+00 0.999854
## S.npnct02.log 0.000e+00 0.999936
## S.npnct08.log 0.000e+00 0.999948
## S.npnct09.log 0.000e+00 0.999950
## A.one.log 0.000e+00 0.999933
## S.one.log 0.000e+00 0.999933
## H.npnct11.log 0.000e+00 0.999955
## S.npnct15.log 0.000e+00 0.999958
## A.npnct15.log 0.000e+00 0.999959
## A.npnct18.log 0.000e+00 0.999968
## H.npnct22.log 0.000e+00 0.999952
## A.npnct19.log 0.000e+00 0.999981
##
## (Intercept) **
## A.npnct23.log
## WordCount.log ***
## `myCategory.fctrForeign#World#Asia Pacific` ***
## `myCategory.fctr#Multimedia#` ***
## `myCategory.fctrCulture#Arts#` ***
## `myCategory.fctrBusiness#Business Day#Dealbook` ***
## myCategory.fctrmyOther ***
## `myCategory.fctrBusiness#Technology#` ***
## `myCategory.fctrBusiness#Crosswords/Games#` .
## `myCategory.fctrTStyle##` ***
## `myCategory.fctrForeign#World#`
## `myCategory.fctrOpEd#Opinion#` ***
## `myCategory.fctrStyles##Fashion` ***
## `myCategory.fctr#Opinion#Room For Debate` ***
## `myCategory.fctr#U.S.#Education`
## `myCategory.fctr##` ***
## `myCategory.fctrMetro#N.Y. / Region#` ***
## `myCategory.fctrBusiness#Business Day#Small Business` ***
## `myCategory.fctrStyles#U.S.#`
## `myCategory.fctrTravel#Travel#` ***
## `myCategory.fctr#Opinion#The Public Editor`
## H.npnct21.log ***
## A.npnct21.log ***
## S.nuppr.log ***
## A.npnct14.log ***
## H.today.log ***
## H.nuppr.log **
## H.npnct09.log **
## PubDate.wkday.fctr1
## PubDate.wkday.fctr2 *
## PubDate.wkday.fctr3
## PubDate.wkday.fctr4
## PubDate.wkday.fctr5 .
## PubDate.wkday.fctr6 **
## S.ndgts.log *
## H.nchrs.log **
## S.report.log
## A.said.log ***
## S.newyork.log
## H.npnct12.log *
## H.npnct04.log
## PubDate.last10.log *
## H.ndgts.log
## H.nwrds.log
## H.npnct17.log
## S.npnct04.log .
## `PubDate.second.fctr(14.8,29.5]`
## `PubDate.second.fctr(29.5,44.2]`
## `PubDate.second.fctr(44.2,59.1]`
## S.can.log **
## S.share.log
## H.new.log
## S.will.log
## H.npnct07.log
## S.take.log
## `PubDate.minute.fctr(14.8,29.5]`
## `PubDate.minute.fctr(29.5,44.2]`
## `PubDate.minute.fctr(44.2,59.1]`
## PubDate.last1.log
## S.npnct01.log .
## H.npnct13.log
## H.day.log
## H.npnct14.log
## S.npnct12.log
## S.compani.log
## H.npnct01.log
## A.nwrds.unq.log
## S.show.log
## `PubDate.hour.fctr(7.67,15.3]`
## `PubDate.hour.fctr(15.3,23]`
## S.presid.log
## S.npnct16.log
## S.make.log
## S.state.log
## A.state.log
## A.npnct27.log
## S.year.log
## H.npnct16.log
## S.intern.log
## H.has.ebola
## `PubDate.date.fctr(7,13]`
## `PubDate.date.fctr(13,19]`
## `PubDate.date.fctr(19,25]`
## `PubDate.date.fctr(25,31]`
## H.report.log
## H.X2014.log
## S.npnct13.log
## H.week.log
## S.time.log
## PubDate.wkend
## S.week.log
## A.nwrds.log
## A.nchrs.log
## PubDate.last100.log
## S.day.log
## A.npnct17.log
## S.first.log
## H.newyork.log
## S.articl.log
## H.daili.log
## H.npnct30.log
## .rnorm
## S.new.log
## S.npnct06.log
## S.fashion.log
## H.npnct15.log
## S.npnct30.log
## A.has.year.colon
## H.npnct02.log
## S.npnct22.log
## S.npnct03.log
## H.npnct05.log
## A.npnct07.log
## S.npnct02.log
## S.npnct08.log
## S.npnct09.log
## A.one.log
## S.one.log
## H.npnct11.log
## S.npnct15.log
## A.npnct15.log
## A.npnct18.log
## H.npnct22.log
## A.npnct19.log
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5900.1 on 6531 degrees of freedom
## Residual deviance: 13724.4 on 6407 degrees of freedom
## AIC: 13974
##
## Number of Fisher Scoring iterations: 25
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.2866885
## 2 0.1 0.6225084
## 3 0.2 0.6900369
## 4 0.3 0.7005240
## 5 0.4 0.6879036
## 6 0.5 0.6773897
## 7 0.6 0.6518160
## 8 0.7 0.5969839
## 9 0.8 0.4994273
## 10 0.9 0.3085106
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Final.glm.N
## 1 N 4920
## 2 Y 224
## Popular.fctr.predict.Final.glm.Y
## 1 519
## 2 869
## Prediction
## Reference N Y
## N 4920 519
## Y 224 869
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.862523e-01 6.315383e-01 8.783030e-01 8.938549e-01 8.326699e-01
## AccuracyPValue McnemarPValue
## 2.451977e-34 4.016717e-27## Warning in mypredict_mdl(mdl, df = fit_df, rsp_var, rsp_var_out,
## model_id_method, : Expecting 1 metric: Accuracy; recd: Accuracy, Kappa;
## retaining Accuracy only
## model_id model_method
## 1 Final.glm glm
## feats
## 1 A.npnct23.log, WordCount.log, myCategory.fctr, H.npnct21.log, A.npnct21.log, S.nuppr.log, A.npnct14.log, H.today.log, H.nuppr.log, H.npnct09.log, PubDate.wkday.fctr, S.ndgts.log, H.nchrs.log, S.report.log, A.said.log, S.newyork.log, H.npnct12.log, H.npnct04.log, PubDate.last10.log, H.ndgts.log, H.nwrds.log, H.npnct17.log, S.npnct04.log, PubDate.second.fctr, S.can.log, S.share.log, H.new.log, S.will.log, H.npnct07.log, S.take.log, PubDate.minute.fctr, PubDate.last1.log, S.npnct01.log, H.npnct13.log, H.day.log, H.npnct14.log, S.npnct12.log, S.compani.log, H.npnct01.log, A.nwrds.unq.log, S.show.log, PubDate.hour.fctr, S.presid.log, S.npnct16.log, S.make.log, S.state.log, A.state.log, A.npnct27.log, S.year.log, H.npnct16.log, S.intern.log, H.has.ebola, PubDate.date.fctr, H.report.log, H.X2014.log, S.npnct13.log, H.week.log, S.time.log, PubDate.wkend, S.week.log, A.nwrds.log, A.nchrs.log, PubDate.last100.log, S.day.log, A.npnct17.log, S.first.log, H.newyork.log, S.articl.log, H.daili.log, H.npnct30.log, .rnorm, S.new.log, S.npnct06.log, S.fashion.log, H.npnct15.log, S.npnct30.log, A.has.year.colon, H.npnct02.log, S.npnct22.log, S.npnct03.log, H.npnct05.log, A.npnct07.log, S.npnct02.log, S.npnct08.log, S.npnct09.log, A.one.log, S.one.log, H.npnct11.log, S.npnct15.log, A.npnct15.log, A.npnct18.log, H.npnct22.log, A.npnct19.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 17.706 5.825
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9171177 0.3 0.700524 0.9043152
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit min.aic.fit
## 1 0.878303 0.8938549 0.6492598 13974.45
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01360071 0.04432099glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 13 fit.data.training 7 0 569.038 594.083 25.045
## 14 fit.data.training 7 1 594.083 NA NAglb_trnent_df <- glb_get_predictions(df=glb_trnent_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(df = glb_trnent_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.3glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
entity_df=glb_trnent_df)
glb_feats_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_feats_df$importance
print(glb_feats_df)## id importance cor.y
## A.npnct23.log A.npnct23.log 1.000000e+02 1.537569e-02
## WordCount.log WordCount.log 1.288801e-05 2.649604e-01
## myCategory.fctr myCategory.fctr 1.002204e-05 1.234541e-02
## H.npnct21.log H.npnct21.log 5.018143e-06 1.283641e-01
## A.npnct21.log A.npnct21.log 4.625851e-06 5.482747e-02
## S.nuppr.log S.nuppr.log 4.569486e-06 -2.718459e-01
## A.npnct14.log A.npnct14.log 3.906452e-06 -4.999563e-02
## H.today.log H.today.log 3.716137e-06 -6.372306e-02
## H.nuppr.log H.nuppr.log 3.182025e-06 -1.278085e-01
## H.npnct09.log H.npnct09.log 3.059125e-06 5.375262e-02
## PubDate.wkday.fctr PubDate.wkday.fctr 2.985990e-06 -3.980129e-02
## S.ndgts.log S.ndgts.log 2.333324e-06 -1.242046e-01
## H.nchrs.log H.nchrs.log 2.327769e-06 -1.710624e-01
## S.report.log S.report.log 2.241758e-06 -5.032801e-02
## A.said.log A.said.log 2.225691e-06 3.735051e-04
## S.newyork.log S.newyork.log 2.215953e-06 -6.219997e-02
## H.npnct12.log H.npnct12.log 2.149261e-06 1.333613e-02
## H.npnct04.log H.npnct04.log 2.100497e-06 -5.126277e-02
## PubDate.last10.log PubDate.last10.log 1.997887e-06 4.931702e-02
## H.ndgts.log H.ndgts.log 1.993583e-06 -1.196633e-01
## H.nwrds.log H.nwrds.log 1.855196e-06 -2.006864e-01
## H.npnct17.log H.npnct17.log 1.847313e-06 3.039622e-02
## S.npnct04.log S.npnct04.log 1.760068e-06 -6.294642e-02
## PubDate.second.fctr PubDate.second.fctr 1.752346e-06 -1.187946e-02
## S.can.log S.can.log 1.698109e-06 3.077833e-02
## S.share.log S.share.log 1.577347e-06 -5.138139e-02
## H.new.log H.new.log 1.501228e-06 -5.313316e-02
## S.will.log S.will.log 1.438120e-06 -6.103349e-02
## H.npnct07.log H.npnct07.log 1.251748e-06 -1.201741e-02
## S.take.log S.take.log 1.242853e-06 -2.569295e-02
## PubDate.minute.fctr PubDate.minute.fctr 1.202598e-06 -3.407385e-02
## PubDate.last1.log PubDate.last1.log 1.200389e-06 4.635751e-02
## S.npnct01.log S.npnct01.log 1.175535e-06 3.093101e-02
## H.npnct13.log H.npnct13.log 1.166146e-06 -1.305305e-02
## H.day.log H.day.log 1.165449e-06 -6.272898e-02
## H.npnct14.log H.npnct14.log 1.162212e-06 -2.524770e-02
## S.npnct12.log S.npnct12.log 1.145878e-06 -9.158156e-02
## S.compani.log S.compani.log 1.136156e-06 -5.261812e-02
## H.npnct01.log H.npnct01.log 1.095863e-06 2.271577e-02
## A.nwrds.unq.log A.nwrds.unq.log 1.074022e-06 -2.506012e-01
## S.show.log S.show.log 1.071063e-06 -4.897915e-02
## PubDate.hour.fctr PubDate.hour.fctr 1.052433e-06 1.354368e-01
## S.presid.log S.presid.log 1.031221e-06 -2.014404e-02
## S.npnct16.log S.npnct16.log 1.022348e-06 -6.770952e-02
## S.make.log S.make.log 1.008816e-06 2.334962e-02
## S.state.log S.state.log 9.110520e-07 7.050791e-03
## A.state.log A.state.log 9.110520e-07 6.668101e-03
## A.npnct27.log A.npnct27.log 9.110520e-07 -5.547032e-03
## S.year.log S.year.log 8.851511e-07 -5.094457e-02
## H.npnct16.log H.npnct16.log 8.710662e-07 -8.273237e-02
## S.intern.log S.intern.log 8.387696e-07 -6.864274e-02
## H.has.ebola H.has.ebola 8.241580e-07 2.588140e-02
## PubDate.date.fctr PubDate.date.fctr 8.233073e-07 -1.164756e-02
## H.report.log H.report.log 8.039331e-07 -6.494810e-02
## H.X2014.log H.X2014.log 7.740347e-07 -4.620638e-02
## S.npnct13.log S.npnct13.log 7.695263e-07 -3.638891e-02
## H.week.log H.week.log 7.110312e-07 -7.510522e-02
## S.time.log S.time.log 6.384092e-07 -5.759227e-02
## PubDate.wkend PubDate.wkend 6.089193e-07 1.067288e-01
## S.week.log S.week.log 5.723540e-07 -8.840293e-02
## A.nwrds.log A.nwrds.log 5.580949e-07 -2.450733e-01
## A.nchrs.log A.nchrs.log 5.287682e-07 -2.245488e-01
## PubDate.last100.log PubDate.last100.log 5.212444e-07 -7.663322e-03
## S.day.log S.day.log 3.145965e-07 -4.555421e-02
## A.npnct17.log A.npnct17.log 3.000151e-07 -1.587454e-03
## S.first.log S.first.log 2.555518e-07 -5.345938e-02
## H.newyork.log H.newyork.log 1.800829e-07 -5.797009e-02
## S.articl.log S.articl.log 1.796068e-07 -5.952055e-02
## H.daili.log H.daili.log 1.287108e-07 -6.919298e-02
## H.npnct30.log H.npnct30.log 9.686374e-08 -8.917338e-02
## .rnorm .rnorm 8.784761e-08 1.756172e-02
## S.new.log S.new.log 4.862671e-08 -3.483189e-02
## S.npnct06.log S.npnct06.log 2.225651e-08 -2.389145e-02
## S.fashion.log S.fashion.log 1.098049e-09 -8.724932e-02
## H.npnct15.log H.npnct15.log 7.395235e-10 -6.158577e-02
## S.npnct30.log S.npnct30.log 5.609428e-10 -4.370037e-02
## A.has.year.colon A.has.year.colon 2.912418e-10 -1.755336e-02
## H.npnct02.log H.npnct02.log 2.666142e-10 -2.001851e-02
## S.npnct22.log S.npnct22.log 2.461340e-10 -1.923169e-02
## S.npnct03.log S.npnct03.log 2.304835e-10 -1.240734e-02
## H.npnct05.log H.npnct05.log 1.846671e-10 -9.653967e-03
## A.npnct07.log A.npnct07.log 1.653979e-10 -1.214357e-02
## S.npnct02.log S.npnct02.log 7.575296e-11 -5.547032e-03
## S.npnct08.log S.npnct08.log 7.353677e-11 -2.413868e-03
## S.npnct09.log S.npnct09.log 6.715377e-11 -3.986882e-03
## A.one.log A.one.log 6.660788e-11 4.368856e-03
## S.one.log S.one.log 6.580549e-11 4.891059e-03
## H.npnct11.log H.npnct11.log 6.416570e-11 -5.547032e-03
## S.npnct15.log S.npnct15.log 5.392046e-12 -2.121844e-02
## A.npnct15.log A.npnct15.log 4.905261e-12 -2.407715e-02
## A.npnct18.log A.npnct18.log 1.783598e-12 -1.451467e-02
## H.npnct22.log H.npnct22.log 1.467473e-12 -5.547032e-03
## A.npnct19.log A.npnct19.log 0.000000e+00 -1.271661e-02
## A.articl.log A.articl.log NA -5.952055e-02
## A.can.log A.can.log NA 3.169296e-02
## A.compani.log A.compani.log NA -5.268413e-02
## A.day.log A.day.log NA -4.581783e-02
## A.fashion.log A.fashion.log NA -8.724932e-02
## A.first.log A.first.log NA -5.345938e-02
## A.has.http A.has.http NA -1.359260e-02
## A.intern.log A.intern.log NA -6.864274e-02
## A.make.log A.make.log NA 2.334962e-02
## A.ndgts.log A.ndgts.log NA -1.249484e-01
## A.new.log A.new.log NA -3.524871e-02
## A.newyork.log A.newyork.log NA -6.219997e-02
## A.npnct01.log A.npnct01.log NA 3.093101e-02
## A.npnct02.log A.npnct02.log NA -1.451467e-02
## A.npnct03.log A.npnct03.log NA -1.359260e-02
## A.npnct04.log A.npnct04.log NA -6.294642e-02
## A.npnct05.log A.npnct05.log NA NA
## A.npnct06.log A.npnct06.log NA -2.389145e-02
## A.npnct08.log A.npnct08.log NA -3.258100e-03
## A.npnct09.log A.npnct09.log NA -4.775988e-03
## A.npnct10.log A.npnct10.log NA NA
## A.npnct11.log A.npnct11.log NA -5.547032e-03
## A.npnct12.log A.npnct12.log NA -9.183870e-02
## A.npnct13.log A.npnct13.log NA -3.760012e-02
## A.npnct16.log A.npnct16.log NA -6.893301e-02
## A.npnct20.log A.npnct20.log NA -1.451467e-02
## A.npnct22.log A.npnct22.log NA -1.923169e-02
## A.npnct24.log A.npnct24.log NA NA
## A.npnct25.log A.npnct25.log NA 1.537569e-02
## A.npnct26.log A.npnct26.log NA -9.890046e-19
## A.npnct28.log A.npnct28.log NA NA
## A.npnct29.log A.npnct29.log NA NA
## A.npnct30.log A.npnct30.log NA -4.373349e-02
## A.npnct31.log A.npnct31.log NA NA
## A.npnct32.log A.npnct32.log NA NA
## A.nuppr.log A.nuppr.log NA -2.720962e-01
## A.presid.log A.presid.log NA -2.014404e-02
## A.report.log A.report.log NA -5.032801e-02
## A.share.log A.share.log NA -5.138139e-02
## A.show.log A.show.log NA -4.897915e-02
## A.take.log A.take.log NA -2.601772e-02
## A.time.log A.time.log NA -5.779371e-02
## A.week.log A.week.log NA -8.840293e-02
## A.will.log A.will.log NA -6.147068e-02
## A.year.log A.year.log NA -5.094457e-02
## H.fashion.log H.fashion.log NA -8.204998e-02
## H.has.http H.has.http NA NA
## H.has.year.colon H.has.year.colon NA -7.842875e-02
## H.npnct03.log H.npnct03.log NA 9.533020e-03
## H.npnct06.log H.npnct06.log NA 3.190718e-02
## H.npnct08.log H.npnct08.log NA 5.375262e-02
## H.npnct10.log H.npnct10.log NA NA
## H.npnct18.log H.npnct18.log NA NA
## H.npnct19.log H.npnct19.log NA NA
## H.npnct20.log H.npnct20.log NA NA
## H.npnct23.log H.npnct23.log NA NA
## H.npnct24.log H.npnct24.log NA NA
## H.npnct25.log H.npnct25.log NA NA
## H.npnct26.log H.npnct26.log NA -9.890046e-19
## H.npnct27.log H.npnct27.log NA NA
## H.npnct28.log H.npnct28.log NA NA
## H.npnct29.log H.npnct29.log NA NA
## H.npnct31.log H.npnct31.log NA NA
## H.npnct32.log H.npnct32.log NA NA
## H.nwrds.unq.log H.nwrds.unq.log NA -2.044964e-01
## H.X2015.log H.X2015.log NA -6.658489e-02
## Popular Popular NA 1.000000e+00
## Popular.fctr Popular.fctr NA NA
## PubDate.last1 PubDate.last1 NA 3.592267e-02
## PubDate.last10 PubDate.last10 NA 5.398093e-02
## PubDate.last100 PubDate.last100 NA 3.989229e-02
## PubDate.month.fctr PubDate.month.fctr NA 1.914874e-02
## PubDate.POSIX PubDate.POSIX NA 1.568326e-02
## PubDate.year.fctr PubDate.year.fctr NA NA
## PubDate.zoo PubDate.zoo NA 1.568326e-02
## S.has.http S.has.http NA NA
## S.has.year.colon S.has.year.colon NA -1.755336e-02
## S.nchrs.log S.nchrs.log NA -2.246930e-01
## S.npnct05.log S.npnct05.log NA NA
## S.npnct07.log S.npnct07.log NA -1.214357e-02
## S.npnct10.log S.npnct10.log NA NA
## S.npnct11.log S.npnct11.log NA -5.547032e-03
## S.npnct14.log S.npnct14.log NA -5.332519e-02
## S.npnct17.log S.npnct17.log NA -1.587454e-03
## S.npnct18.log S.npnct18.log NA NA
## S.npnct19.log S.npnct19.log NA NA
## S.npnct20.log S.npnct20.log NA NA
## S.npnct21.log S.npnct21.log NA 5.503894e-02
## S.npnct23.log S.npnct23.log NA 2.760321e-02
## S.npnct24.log S.npnct24.log NA NA
## S.npnct25.log S.npnct25.log NA 2.760321e-02
## S.npnct26.log S.npnct26.log NA -9.890046e-19
## S.npnct27.log S.npnct27.log NA NA
## S.npnct28.log S.npnct28.log NA NA
## S.npnct29.log S.npnct29.log NA NA
## S.npnct31.log S.npnct31.log NA NA
## S.npnct32.log S.npnct32.log NA NA
## S.nwrds.log S.nwrds.log NA -2.453541e-01
## S.nwrds.unq.log S.nwrds.unq.log NA -2.507969e-01
## S.said.log S.said.log NA 3.735051e-04
## UniqueID UniqueID NA 1.182492e-02
## WordCount WordCount NA 2.575265e-01
## exclude.as.feat cor.y.abs cor.high.X freqRatio
## A.npnct23.log FALSE 1.537569e-02 <NA> 3264.500000
## WordCount.log FALSE 2.649604e-01 <NA> 1.266667
## myCategory.fctr FALSE 1.234541e-02 <NA> 1.337185
## H.npnct21.log FALSE 1.283641e-01 <NA> 14.995098
## A.npnct21.log FALSE 5.482747e-02 <NA> 12.798715
## S.nuppr.log FALSE 2.718459e-01 <NA> 1.152620
## A.npnct14.log FALSE 4.999563e-02 <NA> 4.603330
## H.today.log FALSE 6.372306e-02 <NA> 36.757225
## H.nuppr.log FALSE 1.278085e-01 <NA> 1.033930
## H.npnct09.log FALSE 5.375262e-02 <NA> 111.620690
## PubDate.wkday.fctr FALSE 3.980129e-02 <NA> 1.003268
## S.ndgts.log FALSE 1.242046e-01 <NA> 10.511247
## H.nchrs.log FALSE 1.710624e-01 <NA> 1.023810
## S.report.log FALSE 5.032801e-02 <NA> 24.204633
## A.said.log FALSE 3.735051e-04 <NA> 25.212851
## S.newyork.log FALSE 6.219997e-02 <NA> 15.153465
## H.npnct12.log FALSE 1.333613e-02 <NA> 4.937442
## H.npnct04.log FALSE 5.126277e-02 <NA> 38.325301
## PubDate.last10.log FALSE 4.931702e-02 <NA> 1.666667
## H.ndgts.log FALSE 1.196633e-01 <NA> 13.616137
## H.nwrds.log FALSE 2.006864e-01 <NA> 1.019119
## H.npnct17.log FALSE 3.039622e-02 <NA> 96.104478
## S.npnct04.log FALSE 6.294642e-02 <NA> 28.536364
## PubDate.second.fctr FALSE 1.187946e-02 <NA> 1.018204
## S.can.log FALSE 3.077833e-02 <NA> 26.058091
## S.share.log FALSE 5.138139e-02 <NA> 32.654639
## H.new.log FALSE 5.313316e-02 <NA> 25.228916
## S.will.log FALSE 6.103349e-02 <NA> 11.237288
## H.npnct07.log FALSE 1.201741e-02 <NA> 5.437234
## S.take.log FALSE 2.569295e-02 <NA> 29.376744
## PubDate.minute.fctr FALSE 3.407385e-02 <NA> 1.483365
## PubDate.last1.log FALSE 4.635751e-02 <NA> 1.142857
## S.npnct01.log FALSE 3.093101e-02 <NA> 309.952381
## H.npnct13.log FALSE 1.305305e-02 <NA> 13.126638
## H.day.log FALSE 6.272898e-02 <NA> 29.801887
## H.npnct14.log FALSE 2.524770e-02 <NA> 22.802326
## S.npnct12.log FALSE 9.158156e-02 <NA> 1.660473
## S.compani.log FALSE 5.261812e-02 <NA> 18.093842
## H.npnct01.log FALSE 2.271577e-02 <NA> 282.913043
## A.nwrds.unq.log FALSE 2.506012e-01 <NA> 1.061567
## S.show.log FALSE 4.897915e-02 <NA> 30.512077
## PubDate.hour.fctr FALSE 1.354368e-01 <NA> 1.835040
## S.presid.log FALSE 2.014404e-02 <NA> 26.854701
## S.npnct16.log FALSE 6.770952e-02 <NA> 13.647191
## S.make.log FALSE 2.334962e-02 <NA> 27.378261
## S.state.log FALSE 7.050791e-03 <NA> 30.655340
## A.state.log FALSE 6.668101e-03 <NA> 30.502415
## A.npnct27.log FALSE 5.547032e-03 <NA> 6531.000000
## S.year.log FALSE 5.094457e-02 <NA> 18.456716
## H.npnct16.log FALSE 8.273237e-02 <NA> 3.914910
## S.intern.log FALSE 6.864274e-02 <NA> 29.801887
## H.has.ebola FALSE 2.588140e-02 <NA> 73.227273
## PubDate.date.fctr FALSE 1.164756e-02 <NA> 1.021394
## H.report.log FALSE 6.494810e-02 <NA> 30.403846
## H.X2014.log FALSE 4.620638e-02 <NA> 63.673267
## S.npnct13.log FALSE 3.638891e-02 <NA> 5.706263
## H.week.log FALSE 7.510522e-02 <NA> 24.818182
## S.time.log FALSE 5.759227e-02 <NA> 13.483296
## PubDate.wkend FALSE 1.067288e-01 <NA> 9.095827
## S.week.log FALSE 8.840293e-02 <NA> 13.278509
## A.nwrds.log FALSE 2.450733e-01 <NA> 1.029183
## A.nchrs.log FALSE 2.245488e-01 <NA> 1.328571
## PubDate.last100.log FALSE 7.663322e-03 <NA> 25.000000
## S.day.log FALSE 4.555421e-02 <NA> 24.692913
## A.npnct17.log FALSE 1.587454e-03 <NA> 434.133333
## S.first.log FALSE 5.345938e-02 <NA> 29.509346
## H.newyork.log FALSE 5.797009e-02 <NA> 26.795745
## S.articl.log FALSE 5.952055e-02 <NA> 30.863415
## H.daili.log FALSE 6.919298e-02 <NA> 41.973684
## H.npnct30.log FALSE 8.917338e-02 <NA> 24.123077
## .rnorm FALSE 1.756172e-02 <NA> 1.000000
## S.new.log FALSE 3.483189e-02 <NA> 10.124573
## S.npnct06.log FALSE 2.389145e-02 <NA> 115.642857
## S.fashion.log FALSE 8.724932e-02 <NA> 25.737705
## H.npnct15.log FALSE 6.158577e-02 <NA> 52.983471
## S.npnct30.log FALSE 4.370037e-02 <NA> 134.791667
## A.has.year.colon FALSE 1.755336e-02 <NA> 652.200000
## H.npnct02.log FALSE 2.001851e-02 <NA> 501.461538
## S.npnct22.log FALSE 1.923169e-02 <NA> 543.333333
## S.npnct03.log FALSE 1.240734e-02 <NA> 1305.400000
## H.npnct05.log FALSE 9.653967e-03 <NA> 543.333333
## A.npnct07.log FALSE 1.214357e-02 <NA> 1631.750000
## S.npnct02.log FALSE 5.547032e-03 <NA> 6531.000000
## S.npnct08.log FALSE 2.413868e-03 <NA> 175.513514
## S.npnct09.log FALSE 3.986882e-03 <NA> 175.486486
## A.one.log FALSE 4.368856e-03 <NA> 22.773723
## S.one.log FALSE 4.891059e-03 <NA> 22.777372
## H.npnct11.log FALSE 5.547032e-03 <NA> 6531.000000
## S.npnct15.log FALSE 2.121844e-02 <NA> 203.062500
## A.npnct15.log FALSE 2.407715e-02 <NA> 196.696970
## A.npnct18.log FALSE 1.451467e-02 <NA> 1087.500000
## H.npnct22.log FALSE 5.547032e-03 <NA> 6531.000000
## A.npnct19.log FALSE 1.271661e-02 <NA> 1631.500000
## A.articl.log FALSE 5.952055e-02 S.articl.log 30.863415
## A.can.log FALSE 3.169296e-02 S.can.log 26.166667
## A.compani.log FALSE 5.268413e-02 S.compani.log 18.147059
## A.day.log FALSE 4.581783e-02 S.day.log 24.592157
## A.fashion.log FALSE 8.724932e-02 S.fashion.log 25.737705
## A.first.log FALSE 5.345938e-02 S.first.log 29.509346
## A.has.http FALSE 1.359260e-02 <NA> 1087.666667
## A.intern.log FALSE 6.864274e-02 S.intern.log 29.801887
## A.make.log FALSE 2.334962e-02 S.make.log 27.378261
## A.ndgts.log FALSE 1.249484e-01 S.ndgts.log 10.501022
## A.new.log FALSE 3.524871e-02 S.new.log 10.086735
## A.newyork.log FALSE 6.219997e-02 S.newyork.log 15.153465
## A.npnct01.log FALSE 3.093101e-02 S.npnct01.log 309.952381
## A.npnct02.log FALSE 1.451467e-02 <NA> 1087.500000
## A.npnct03.log FALSE 1.359260e-02 <NA> 1087.666667
## A.npnct04.log FALSE 6.294642e-02 S.npnct04.log 28.536364
## A.npnct05.log FALSE NA <NA> 0.000000
## A.npnct06.log FALSE 2.389145e-02 S.npnct06.log 115.642857
## A.npnct08.log FALSE 3.258100e-03 <NA> 170.868421
## A.npnct09.log FALSE 4.775988e-03 <NA> 170.842105
## A.npnct10.log FALSE NA <NA> 0.000000
## A.npnct11.log FALSE 5.547032e-03 <NA> 6531.000000
## A.npnct12.log FALSE 9.183870e-02 S.npnct12.log 1.660473
## A.npnct13.log FALSE 3.760012e-02 S.npnct13.log 5.715368
## A.npnct16.log FALSE 6.893301e-02 S.npnct16.log 13.482222
## A.npnct20.log FALSE 1.451467e-02 <NA> 1087.500000
## A.npnct22.log FALSE 1.923169e-02 S.npnct22.log 543.333333
## A.npnct24.log FALSE NA <NA> 0.000000
## A.npnct25.log FALSE 1.537569e-02 <NA> 3264.500000
## A.npnct26.log FALSE 9.890046e-19 <NA> 0.000000
## A.npnct28.log FALSE NA <NA> 0.000000
## A.npnct29.log FALSE NA <NA> 0.000000
## A.npnct30.log FALSE 4.373349e-02 S.npnct30.log 126.862745
## A.npnct31.log FALSE NA <NA> 0.000000
## A.npnct32.log FALSE NA <NA> 0.000000
## A.nuppr.log FALSE 2.720962e-01 S.nuppr.log 1.151308
## A.presid.log FALSE 2.014404e-02 S.presid.log 26.854701
## A.report.log FALSE 5.032801e-02 S.report.log 24.204633
## A.share.log FALSE 5.138139e-02 S.share.log 32.654639
## A.show.log FALSE 4.897915e-02 S.show.log 30.512077
## A.take.log FALSE 2.601772e-02 S.take.log 29.236111
## A.time.log FALSE 5.779371e-02 S.time.log 13.451111
## A.week.log FALSE 8.840293e-02 S.week.log 13.278509
## A.will.log FALSE 6.147068e-02 S.will.log 11.212406
## A.year.log FALSE 5.094457e-02 S.year.log 18.456716
## H.fashion.log FALSE 8.204998e-02 H.week.log 28.542986
## H.has.http FALSE NA <NA> 0.000000
## H.has.year.colon FALSE 7.842875e-02 A.intern.log 32.670103
## H.npnct03.log FALSE 9.533020e-03 <NA> 2176.333333
## H.npnct06.log FALSE 3.190718e-02 H.npnct17.log 68.935484
## H.npnct08.log FALSE 5.375262e-02 H.npnct09.log 111.620690
## H.npnct10.log FALSE NA <NA> 0.000000
## H.npnct18.log FALSE NA <NA> 0.000000
## H.npnct19.log FALSE NA <NA> 0.000000
## H.npnct20.log FALSE NA <NA> 0.000000
## H.npnct23.log FALSE NA <NA> 0.000000
## H.npnct24.log FALSE NA <NA> 0.000000
## H.npnct25.log FALSE NA <NA> 0.000000
## H.npnct26.log FALSE 9.890046e-19 <NA> 0.000000
## H.npnct27.log FALSE NA <NA> 0.000000
## H.npnct28.log FALSE NA <NA> 0.000000
## H.npnct29.log FALSE NA <NA> 0.000000
## H.npnct31.log FALSE NA <NA> 0.000000
## H.npnct32.log FALSE NA <NA> 0.000000
## H.nwrds.unq.log FALSE 2.044964e-01 H.nuppr.log 1.019071
## H.X2015.log FALSE 6.658489e-02 H.npnct15.log 45.326241
## Popular TRUE 1.000000e+00 <NA> 4.976212
## Popular.fctr TRUE NA <NA> NA
## PubDate.last1 TRUE 3.592267e-02 <NA> 1.142857
## PubDate.last10 TRUE 5.398093e-02 <NA> 1.666667
## PubDate.last100 TRUE 3.989229e-02 <NA> 25.000000
## PubDate.month.fctr TRUE 1.914874e-02 <NA> 1.017514
## PubDate.POSIX TRUE 1.568326e-02 <NA> 1.000000
## PubDate.year.fctr FALSE NA <NA> 0.000000
## PubDate.zoo TRUE 1.568326e-02 <NA> 1.000000
## S.has.http FALSE NA <NA> 0.000000
## S.has.year.colon FALSE 1.755336e-02 <NA> 652.200000
## S.nchrs.log FALSE 2.246930e-01 A.nchrs.log 1.328571
## S.npnct05.log FALSE NA <NA> 0.000000
## S.npnct07.log FALSE 1.214357e-02 <NA> 1631.750000
## S.npnct10.log FALSE NA <NA> 0.000000
## S.npnct11.log FALSE 5.547032e-03 <NA> 6531.000000
## S.npnct14.log FALSE 5.332519e-02 A.npnct14.log 4.672000
## S.npnct17.log FALSE 1.587454e-03 <NA> 434.133333
## S.npnct18.log FALSE NA <NA> 0.000000
## S.npnct19.log FALSE NA <NA> 0.000000
## S.npnct20.log FALSE NA <NA> 0.000000
## S.npnct21.log FALSE 5.503894e-02 A.npnct21.log 12.862366
## S.npnct23.log FALSE 2.760321e-02 S.npnct25.log 6531.000000
## S.npnct24.log FALSE NA <NA> 0.000000
## S.npnct25.log FALSE 2.760321e-02 <NA> 6531.000000
## S.npnct26.log FALSE 9.890046e-19 <NA> 0.000000
## S.npnct27.log FALSE NA <NA> 0.000000
## S.npnct28.log FALSE NA <NA> 0.000000
## S.npnct29.log FALSE NA <NA> 0.000000
## S.npnct31.log FALSE NA <NA> 0.000000
## S.npnct32.log FALSE NA <NA> 0.000000
## S.nwrds.log FALSE 2.453541e-01 A.nwrds.log 1.029183
## S.nwrds.unq.log FALSE 2.507969e-01 S.nchrs.log 1.061567
## S.said.log FALSE 3.735051e-04 <NA> 25.212851
## UniqueID TRUE 1.182492e-02 <NA> 1.000000
## WordCount TRUE 2.575265e-01 <NA> 2.315789
## percentUnique zeroVar nzv is.cor.y.abs.low
## A.npnct23.log 0.04592774 FALSE TRUE TRUE
## WordCount.log 24.15799143 FALSE FALSE FALSE
## myCategory.fctr 0.30618494 FALSE FALSE TRUE
## H.npnct21.log 0.06123699 FALSE FALSE FALSE
## A.npnct21.log 0.07654623 FALSE FALSE FALSE
## S.nuppr.log 0.33680343 FALSE FALSE FALSE
## A.npnct14.log 0.16840171 FALSE FALSE FALSE
## H.today.log 0.03061849 FALSE TRUE FALSE
## H.nuppr.log 0.29087569 FALSE FALSE FALSE
## H.npnct09.log 0.03061849 FALSE TRUE FALSE
## PubDate.wkday.fctr 0.10716473 FALSE FALSE FALSE
## S.ndgts.log 0.26025720 FALSE FALSE FALSE
## H.nchrs.log 1.57685242 FALSE FALSE FALSE
## S.report.log 0.06123699 FALSE TRUE FALSE
## A.said.log 0.04592774 FALSE TRUE TRUE
## S.newyork.log 0.06123699 FALSE FALSE FALSE
## H.npnct12.log 0.07654623 FALSE FALSE TRUE
## H.npnct04.log 0.04592774 FALSE TRUE FALSE
## PubDate.last10.log 79.05695040 FALSE FALSE FALSE
## H.ndgts.log 0.18371096 FALSE FALSE FALSE
## H.nwrds.log 0.21432945 FALSE FALSE FALSE
## H.npnct17.log 0.06123699 FALSE TRUE FALSE
## S.npnct04.log 0.07654623 FALSE TRUE FALSE
## PubDate.second.fctr 0.06123699 FALSE FALSE TRUE
## S.can.log 0.04592774 FALSE TRUE FALSE
## S.share.log 0.04592774 FALSE TRUE FALSE
## H.new.log 0.04592774 FALSE TRUE FALSE
## S.will.log 0.06123699 FALSE FALSE FALSE
## H.npnct07.log 0.12247397 FALSE FALSE TRUE
## S.take.log 0.04592774 FALSE TRUE FALSE
## PubDate.minute.fctr 0.06123699 FALSE FALSE FALSE
## PubDate.last1.log 36.49724434 FALSE FALSE FALSE
## S.npnct01.log 0.06123699 FALSE TRUE FALSE
## H.npnct13.log 0.09185548 FALSE FALSE TRUE
## H.day.log 0.04592774 FALSE TRUE FALSE
## H.npnct14.log 0.12247397 FALSE TRUE FALSE
## S.npnct12.log 0.13778322 FALSE FALSE FALSE
## S.compani.log 0.04592774 FALSE FALSE FALSE
## H.npnct01.log 0.04592774 FALSE TRUE FALSE
## A.nwrds.unq.log 0.55113288 FALSE FALSE FALSE
## S.show.log 0.06123699 FALSE TRUE FALSE
## PubDate.hour.fctr 0.04592774 FALSE FALSE FALSE
## S.presid.log 0.06123699 FALSE TRUE FALSE
## S.npnct16.log 0.04592774 FALSE FALSE FALSE
## S.make.log 0.04592774 FALSE TRUE FALSE
## S.state.log 0.04592774 FALSE TRUE TRUE
## A.state.log 0.04592774 FALSE TRUE TRUE
## A.npnct27.log 0.03061849 FALSE TRUE TRUE
## S.year.log 0.06123699 FALSE FALSE FALSE
## H.npnct16.log 0.04592774 FALSE FALSE FALSE
## S.intern.log 0.04592774 FALSE TRUE FALSE
## H.has.ebola 0.03061849 FALSE TRUE FALSE
## PubDate.date.fctr 0.07654623 FALSE FALSE TRUE
## H.report.log 0.03061849 FALSE TRUE FALSE
## H.X2014.log 0.03061849 FALSE TRUE FALSE
## S.npnct13.log 0.09185548 FALSE FALSE FALSE
## H.week.log 0.03061849 FALSE TRUE FALSE
## S.time.log 0.04592774 FALSE FALSE FALSE
## PubDate.wkend 0.03061849 FALSE FALSE FALSE
## S.week.log 0.04592774 FALSE FALSE FALSE
## A.nwrds.log 0.59706062 FALSE FALSE FALSE
## A.nchrs.log 4.39375383 FALSE FALSE FALSE
## PubDate.last100.log 92.19228414 FALSE FALSE TRUE
## S.day.log 0.04592774 FALSE TRUE FALSE
## A.npnct17.log 0.04592774 FALSE TRUE TRUE
## S.first.log 0.04592774 FALSE TRUE FALSE
## H.newyork.log 0.03061849 FALSE TRUE FALSE
## S.articl.log 0.03061849 FALSE TRUE FALSE
## H.daili.log 0.03061849 FALSE TRUE FALSE
## H.npnct30.log 0.03061849 FALSE TRUE FALSE
## .rnorm 100.00000000 FALSE FALSE FALSE
## S.new.log 0.04592774 FALSE FALSE FALSE
## S.npnct06.log 0.03061849 FALSE TRUE FALSE
## S.fashion.log 0.04592774 FALSE TRUE FALSE
## H.npnct15.log 0.03061849 FALSE TRUE FALSE
## S.npnct30.log 0.04592774 FALSE TRUE FALSE
## A.has.year.colon 0.03061849 FALSE TRUE TRUE
## H.npnct02.log 0.03061849 FALSE TRUE FALSE
## S.npnct22.log 0.03061849 FALSE TRUE FALSE
## S.npnct03.log 0.03061849 FALSE TRUE TRUE
## H.npnct05.log 0.03061849 FALSE TRUE TRUE
## A.npnct07.log 0.04592774 FALSE TRUE TRUE
## S.npnct02.log 0.03061849 FALSE TRUE TRUE
## S.npnct08.log 0.04592774 FALSE TRUE TRUE
## S.npnct09.log 0.06123699 FALSE TRUE TRUE
## A.one.log 0.04592774 FALSE TRUE TRUE
## S.one.log 0.04592774 FALSE TRUE TRUE
## H.npnct11.log 0.03061849 FALSE TRUE TRUE
## S.npnct15.log 0.04592774 FALSE TRUE FALSE
## A.npnct15.log 0.10716473 FALSE TRUE FALSE
## A.npnct18.log 0.04592774 FALSE TRUE TRUE
## H.npnct22.log 0.03061849 FALSE TRUE TRUE
## A.npnct19.log 0.06123699 FALSE TRUE TRUE
## A.articl.log 0.03061849 FALSE TRUE FALSE
## A.can.log 0.04592774 FALSE TRUE FALSE
## A.compani.log 0.04592774 FALSE FALSE FALSE
## A.day.log 0.04592774 FALSE TRUE FALSE
## A.fashion.log 0.04592774 FALSE TRUE FALSE
## A.first.log 0.04592774 FALSE TRUE FALSE
## A.has.http 0.03061849 FALSE TRUE TRUE
## A.intern.log 0.04592774 FALSE TRUE FALSE
## A.make.log 0.04592774 FALSE TRUE FALSE
## A.ndgts.log 0.29087569 FALSE FALSE FALSE
## A.new.log 0.04592774 FALSE FALSE FALSE
## A.newyork.log 0.06123699 FALSE FALSE FALSE
## A.npnct01.log 0.06123699 FALSE TRUE FALSE
## A.npnct02.log 0.04592774 FALSE TRUE TRUE
## A.npnct03.log 0.03061849 FALSE TRUE TRUE
## A.npnct04.log 0.07654623 FALSE TRUE FALSE
## A.npnct05.log 0.01530925 TRUE TRUE NA
## A.npnct06.log 0.03061849 FALSE TRUE FALSE
## A.npnct08.log 0.04592774 FALSE TRUE TRUE
## A.npnct09.log 0.06123699 FALSE TRUE TRUE
## A.npnct10.log 0.01530925 TRUE TRUE NA
## A.npnct11.log 0.03061849 FALSE TRUE TRUE
## A.npnct12.log 0.13778322 FALSE FALSE FALSE
## A.npnct13.log 0.12247397 FALSE FALSE FALSE
## A.npnct16.log 0.04592774 FALSE FALSE FALSE
## A.npnct20.log 0.04592774 FALSE TRUE TRUE
## A.npnct22.log 0.03061849 FALSE TRUE FALSE
## A.npnct24.log 0.01530925 TRUE TRUE NA
## A.npnct25.log 0.04592774 FALSE TRUE TRUE
## A.npnct26.log 0.01530925 TRUE TRUE TRUE
## A.npnct28.log 0.01530925 TRUE TRUE NA
## A.npnct29.log 0.01530925 TRUE TRUE NA
## A.npnct30.log 0.04592774 FALSE TRUE FALSE
## A.npnct31.log 0.01530925 TRUE TRUE NA
## A.npnct32.log 0.01530925 TRUE TRUE NA
## A.nuppr.log 0.33680343 FALSE FALSE FALSE
## A.presid.log 0.06123699 FALSE TRUE FALSE
## A.report.log 0.06123699 FALSE TRUE FALSE
## A.share.log 0.04592774 FALSE TRUE FALSE
## A.show.log 0.06123699 FALSE TRUE FALSE
## A.take.log 0.04592774 FALSE TRUE FALSE
## A.time.log 0.04592774 FALSE FALSE FALSE
## A.week.log 0.04592774 FALSE FALSE FALSE
## A.will.log 0.06123699 FALSE FALSE FALSE
## A.year.log 0.06123699 FALSE FALSE FALSE
## H.fashion.log 0.04592774 FALSE TRUE FALSE
## H.has.http 0.01530925 TRUE TRUE NA
## H.has.year.colon 0.03061849 FALSE TRUE FALSE
## H.npnct03.log 0.03061849 FALSE TRUE TRUE
## H.npnct06.log 0.06123699 FALSE TRUE FALSE
## H.npnct08.log 0.03061849 FALSE TRUE FALSE
## H.npnct10.log 0.01530925 TRUE TRUE NA
## H.npnct18.log 0.01530925 TRUE TRUE NA
## H.npnct19.log 0.01530925 TRUE TRUE NA
## H.npnct20.log 0.01530925 TRUE TRUE NA
## H.npnct23.log 0.01530925 TRUE TRUE NA
## H.npnct24.log 0.01530925 TRUE TRUE NA
## H.npnct25.log 0.01530925 TRUE TRUE NA
## H.npnct26.log 0.01530925 TRUE TRUE TRUE
## H.npnct27.log 0.01530925 TRUE TRUE NA
## H.npnct28.log 0.01530925 TRUE TRUE NA
## H.npnct29.log 0.01530925 TRUE TRUE NA
## H.npnct31.log 0.01530925 TRUE TRUE NA
## H.npnct32.log 0.01530925 TRUE TRUE NA
## H.nwrds.unq.log 0.21432945 FALSE FALSE FALSE
## H.X2015.log 0.03061849 FALSE TRUE FALSE
## Popular 0.03061849 FALSE FALSE FALSE
## Popular.fctr NA NA NA NA
## PubDate.last1 36.49724434 FALSE FALSE FALSE
## PubDate.last10 79.05695040 FALSE FALSE FALSE
## PubDate.last100 92.52908757 FALSE FALSE FALSE
## PubDate.month.fctr 0.04592774 FALSE FALSE FALSE
## PubDate.POSIX 99.86221678 FALSE FALSE TRUE
## PubDate.year.fctr 0.01530925 TRUE TRUE NA
## PubDate.zoo 99.86221678 FALSE FALSE TRUE
## S.has.http 0.01530925 TRUE TRUE NA
## S.has.year.colon 0.03061849 FALSE TRUE TRUE
## S.nchrs.log 3.72014697 FALSE FALSE FALSE
## S.npnct05.log 0.01530925 TRUE TRUE NA
## S.npnct07.log 0.04592774 FALSE TRUE TRUE
## S.npnct10.log 0.01530925 TRUE TRUE NA
## S.npnct11.log 0.03061849 FALSE TRUE TRUE
## S.npnct14.log 0.16840171 FALSE FALSE FALSE
## S.npnct17.log 0.04592774 FALSE TRUE TRUE
## S.npnct18.log 0.01530925 TRUE TRUE NA
## S.npnct19.log 0.01530925 TRUE TRUE NA
## S.npnct20.log 0.01530925 TRUE TRUE NA
## S.npnct21.log 0.07654623 FALSE FALSE FALSE
## S.npnct23.log 0.03061849 FALSE TRUE FALSE
## S.npnct24.log 0.01530925 TRUE TRUE NA
## S.npnct25.log 0.03061849 FALSE TRUE FALSE
## S.npnct26.log 0.01530925 TRUE TRUE TRUE
## S.npnct27.log 0.01530925 TRUE TRUE NA
## S.npnct28.log 0.01530925 TRUE TRUE NA
## S.npnct29.log 0.01530925 TRUE TRUE NA
## S.npnct31.log 0.01530925 TRUE TRUE NA
## S.npnct32.log 0.01530925 TRUE TRUE NA
## S.nwrds.log 0.45927740 FALSE FALSE FALSE
## S.nwrds.unq.log 0.44396816 FALSE FALSE FALSE
## S.said.log 0.04592774 FALSE TRUE TRUE
## UniqueID 100.00000000 FALSE FALSE TRUE
## WordCount 24.15799143 FALSE FALSE FALSE
## rsp_var_raw id_var rsp_var Low.cor.X.glm.importance
## A.npnct23.log FALSE NA NA 1.000000e+02
## WordCount.log FALSE NA NA 1.288801e-05
## myCategory.fctr FALSE NA NA 1.002204e-05
## H.npnct21.log FALSE NA NA 5.018143e-06
## A.npnct21.log FALSE NA NA 4.625851e-06
## S.nuppr.log FALSE NA NA 4.569486e-06
## A.npnct14.log FALSE NA NA 3.906452e-06
## H.today.log FALSE NA NA 3.716137e-06
## H.nuppr.log FALSE NA NA 3.182025e-06
## H.npnct09.log FALSE NA NA 3.059125e-06
## PubDate.wkday.fctr FALSE NA NA 2.985990e-06
## S.ndgts.log FALSE NA NA 2.333324e-06
## H.nchrs.log FALSE NA NA 2.327769e-06
## S.report.log FALSE NA NA 2.241758e-06
## A.said.log FALSE NA NA 2.225691e-06
## S.newyork.log FALSE NA NA 2.215953e-06
## H.npnct12.log FALSE NA NA 2.149261e-06
## H.npnct04.log FALSE NA NA 2.100497e-06
## PubDate.last10.log FALSE NA NA 1.997887e-06
## H.ndgts.log FALSE NA NA 1.993583e-06
## H.nwrds.log FALSE NA NA 1.855196e-06
## H.npnct17.log FALSE NA NA 1.847313e-06
## S.npnct04.log FALSE NA NA 1.760068e-06
## PubDate.second.fctr FALSE NA NA 1.752346e-06
## S.can.log FALSE NA NA 1.698109e-06
## S.share.log FALSE NA NA 1.577347e-06
## H.new.log FALSE NA NA 1.501228e-06
## S.will.log FALSE NA NA 1.438120e-06
## H.npnct07.log FALSE NA NA 1.251748e-06
## S.take.log FALSE NA NA 1.242853e-06
## PubDate.minute.fctr FALSE NA NA 1.202598e-06
## PubDate.last1.log FALSE NA NA 1.200389e-06
## S.npnct01.log FALSE NA NA 1.175535e-06
## H.npnct13.log FALSE NA NA 1.166146e-06
## H.day.log FALSE NA NA 1.165449e-06
## H.npnct14.log FALSE NA NA 1.162212e-06
## S.npnct12.log FALSE NA NA 1.145878e-06
## S.compani.log FALSE NA NA 1.136156e-06
## H.npnct01.log FALSE NA NA 1.095863e-06
## A.nwrds.unq.log FALSE NA NA 1.074022e-06
## S.show.log FALSE NA NA 1.071063e-06
## PubDate.hour.fctr FALSE NA NA 1.052433e-06
## S.presid.log FALSE NA NA 1.031221e-06
## S.npnct16.log FALSE NA NA 1.022348e-06
## S.make.log FALSE NA NA 1.008816e-06
## S.state.log FALSE NA NA 9.110520e-07
## A.state.log FALSE NA NA 9.110520e-07
## A.npnct27.log FALSE NA NA 9.110520e-07
## S.year.log FALSE NA NA 8.851511e-07
## H.npnct16.log FALSE NA NA 8.710662e-07
## S.intern.log FALSE NA NA 8.387696e-07
## H.has.ebola FALSE NA NA 8.241580e-07
## PubDate.date.fctr FALSE NA NA 8.233073e-07
## H.report.log FALSE NA NA 8.039331e-07
## H.X2014.log FALSE NA NA 7.740347e-07
## S.npnct13.log FALSE NA NA 7.695263e-07
## H.week.log FALSE NA NA 7.110312e-07
## S.time.log FALSE NA NA 6.384092e-07
## PubDate.wkend FALSE NA NA 6.089193e-07
## S.week.log FALSE NA NA 5.723540e-07
## A.nwrds.log FALSE NA NA 5.580949e-07
## A.nchrs.log FALSE NA NA 5.287682e-07
## PubDate.last100.log FALSE NA NA 5.212444e-07
## S.day.log FALSE NA NA 3.145965e-07
## A.npnct17.log FALSE NA NA 3.000151e-07
## S.first.log FALSE NA NA 2.555518e-07
## H.newyork.log FALSE NA NA 1.800829e-07
## S.articl.log FALSE NA NA 1.796068e-07
## H.daili.log FALSE NA NA 1.287108e-07
## H.npnct30.log FALSE NA NA 9.686374e-08
## .rnorm FALSE NA NA 8.784761e-08
## S.new.log FALSE NA NA 4.862671e-08
## S.npnct06.log FALSE NA NA 2.225651e-08
## S.fashion.log FALSE NA NA 1.098049e-09
## H.npnct15.log FALSE NA NA 7.395235e-10
## S.npnct30.log FALSE NA NA 5.609428e-10
## A.has.year.colon FALSE NA NA 2.912418e-10
## H.npnct02.log FALSE NA NA 2.666142e-10
## S.npnct22.log FALSE NA NA 2.461340e-10
## S.npnct03.log FALSE NA NA 2.304835e-10
## H.npnct05.log FALSE NA NA 1.846671e-10
## A.npnct07.log FALSE NA NA 1.653979e-10
## S.npnct02.log FALSE NA NA 7.575296e-11
## S.npnct08.log FALSE NA NA 7.353677e-11
## S.npnct09.log FALSE NA NA 6.715377e-11
## A.one.log FALSE NA NA 6.660788e-11
## S.one.log FALSE NA NA 6.580549e-11
## H.npnct11.log FALSE NA NA 6.416570e-11
## S.npnct15.log FALSE NA NA 5.392046e-12
## A.npnct15.log FALSE NA NA 4.905261e-12
## A.npnct18.log FALSE NA NA 1.783598e-12
## H.npnct22.log FALSE NA NA 1.467473e-12
## A.npnct19.log FALSE NA NA 0.000000e+00
## A.articl.log FALSE NA NA NA
## A.can.log FALSE NA NA NA
## A.compani.log FALSE NA NA NA
## A.day.log FALSE NA NA NA
## A.fashion.log FALSE NA NA NA
## A.first.log FALSE NA NA NA
## A.has.http FALSE NA NA NA
## A.intern.log FALSE NA NA NA
## A.make.log FALSE NA NA NA
## A.ndgts.log FALSE NA NA NA
## A.new.log FALSE NA NA NA
## A.newyork.log FALSE NA NA NA
## A.npnct01.log FALSE NA NA NA
## A.npnct02.log FALSE NA NA NA
## A.npnct03.log FALSE NA NA NA
## A.npnct04.log FALSE NA NA NA
## A.npnct05.log FALSE NA NA NA
## A.npnct06.log FALSE NA NA NA
## A.npnct08.log FALSE NA NA NA
## A.npnct09.log FALSE NA NA NA
## A.npnct10.log FALSE NA NA NA
## A.npnct11.log FALSE NA NA NA
## A.npnct12.log FALSE NA NA NA
## A.npnct13.log FALSE NA NA NA
## A.npnct16.log FALSE NA NA NA
## A.npnct20.log FALSE NA NA NA
## A.npnct22.log FALSE NA NA NA
## A.npnct24.log FALSE NA NA NA
## A.npnct25.log FALSE NA NA NA
## A.npnct26.log FALSE NA NA NA
## A.npnct28.log FALSE NA NA NA
## A.npnct29.log FALSE NA NA NA
## A.npnct30.log FALSE NA NA NA
## A.npnct31.log FALSE NA NA NA
## A.npnct32.log FALSE NA NA NA
## A.nuppr.log FALSE NA NA NA
## A.presid.log FALSE NA NA NA
## A.report.log FALSE NA NA NA
## A.share.log FALSE NA NA NA
## A.show.log FALSE NA NA NA
## A.take.log FALSE NA NA NA
## A.time.log FALSE NA NA NA
## A.week.log FALSE NA NA NA
## A.will.log FALSE NA NA NA
## A.year.log FALSE NA NA NA
## H.fashion.log FALSE NA NA NA
## H.has.http FALSE NA NA NA
## H.has.year.colon FALSE NA NA NA
## H.npnct03.log FALSE NA NA NA
## H.npnct06.log FALSE NA NA NA
## H.npnct08.log FALSE NA NA NA
## H.npnct10.log FALSE NA NA NA
## H.npnct18.log FALSE NA NA NA
## H.npnct19.log FALSE NA NA NA
## H.npnct20.log FALSE NA NA NA
## H.npnct23.log FALSE NA NA NA
## H.npnct24.log FALSE NA NA NA
## H.npnct25.log FALSE NA NA NA
## H.npnct26.log FALSE NA NA NA
## H.npnct27.log FALSE NA NA NA
## H.npnct28.log FALSE NA NA NA
## H.npnct29.log FALSE NA NA NA
## H.npnct31.log FALSE NA NA NA
## H.npnct32.log FALSE NA NA NA
## H.nwrds.unq.log FALSE NA NA NA
## H.X2015.log FALSE NA NA NA
## Popular TRUE NA NA NA
## Popular.fctr NA NA TRUE NA
## PubDate.last1 FALSE NA NA NA
## PubDate.last10 FALSE NA NA NA
## PubDate.last100 FALSE NA NA NA
## PubDate.month.fctr FALSE NA NA NA
## PubDate.POSIX FALSE NA NA NA
## PubDate.year.fctr FALSE NA NA NA
## PubDate.zoo FALSE NA NA NA
## S.has.http FALSE NA NA NA
## S.has.year.colon FALSE NA NA NA
## S.nchrs.log FALSE NA NA NA
## S.npnct05.log FALSE NA NA NA
## S.npnct07.log FALSE NA NA NA
## S.npnct10.log FALSE NA NA NA
## S.npnct11.log FALSE NA NA NA
## S.npnct14.log FALSE NA NA NA
## S.npnct17.log FALSE NA NA NA
## S.npnct18.log FALSE NA NA NA
## S.npnct19.log FALSE NA NA NA
## S.npnct20.log FALSE NA NA NA
## S.npnct21.log FALSE NA NA NA
## S.npnct23.log FALSE NA NA NA
## S.npnct24.log FALSE NA NA NA
## S.npnct25.log FALSE NA NA NA
## S.npnct26.log FALSE NA NA NA
## S.npnct27.log FALSE NA NA NA
## S.npnct28.log FALSE NA NA NA
## S.npnct29.log FALSE NA NA NA
## S.npnct31.log FALSE NA NA NA
## S.npnct32.log FALSE NA NA NA
## S.nwrds.log FALSE NA NA NA
## S.nwrds.unq.log FALSE NA NA NA
## S.said.log FALSE NA NA NA
## UniqueID FALSE TRUE NA NA
## WordCount FALSE NA NA NA
## Final.glm.importance
## A.npnct23.log 1.000000e+02
## WordCount.log 1.288801e-05
## myCategory.fctr 1.002204e-05
## H.npnct21.log 5.018143e-06
## A.npnct21.log 4.625851e-06
## S.nuppr.log 4.569486e-06
## A.npnct14.log 3.906452e-06
## H.today.log 3.716137e-06
## H.nuppr.log 3.182025e-06
## H.npnct09.log 3.059125e-06
## PubDate.wkday.fctr 2.985990e-06
## S.ndgts.log 2.333324e-06
## H.nchrs.log 2.327769e-06
## S.report.log 2.241758e-06
## A.said.log 2.225691e-06
## S.newyork.log 2.215953e-06
## H.npnct12.log 2.149261e-06
## H.npnct04.log 2.100497e-06
## PubDate.last10.log 1.997887e-06
## H.ndgts.log 1.993583e-06
## H.nwrds.log 1.855196e-06
## H.npnct17.log 1.847313e-06
## S.npnct04.log 1.760068e-06
## PubDate.second.fctr 1.752346e-06
## S.can.log 1.698109e-06
## S.share.log 1.577347e-06
## H.new.log 1.501228e-06
## S.will.log 1.438120e-06
## H.npnct07.log 1.251748e-06
## S.take.log 1.242853e-06
## PubDate.minute.fctr 1.202598e-06
## PubDate.last1.log 1.200389e-06
## S.npnct01.log 1.175535e-06
## H.npnct13.log 1.166146e-06
## H.day.log 1.165449e-06
## H.npnct14.log 1.162212e-06
## S.npnct12.log 1.145878e-06
## S.compani.log 1.136156e-06
## H.npnct01.log 1.095863e-06
## A.nwrds.unq.log 1.074022e-06
## S.show.log 1.071063e-06
## PubDate.hour.fctr 1.052433e-06
## S.presid.log 1.031221e-06
## S.npnct16.log 1.022348e-06
## S.make.log 1.008816e-06
## S.state.log 9.110520e-07
## A.state.log 9.110520e-07
## A.npnct27.log 9.110520e-07
## S.year.log 8.851511e-07
## H.npnct16.log 8.710662e-07
## S.intern.log 8.387696e-07
## H.has.ebola 8.241580e-07
## PubDate.date.fctr 8.233073e-07
## H.report.log 8.039331e-07
## H.X2014.log 7.740347e-07
## S.npnct13.log 7.695263e-07
## H.week.log 7.110312e-07
## S.time.log 6.384092e-07
## PubDate.wkend 6.089193e-07
## S.week.log 5.723540e-07
## A.nwrds.log 5.580949e-07
## A.nchrs.log 5.287682e-07
## PubDate.last100.log 5.212444e-07
## S.day.log 3.145965e-07
## A.npnct17.log 3.000151e-07
## S.first.log 2.555518e-07
## H.newyork.log 1.800829e-07
## S.articl.log 1.796068e-07
## H.daili.log 1.287108e-07
## H.npnct30.log 9.686374e-08
## .rnorm 8.784761e-08
## S.new.log 4.862671e-08
## S.npnct06.log 2.225651e-08
## S.fashion.log 1.098049e-09
## H.npnct15.log 7.395235e-10
## S.npnct30.log 5.609428e-10
## A.has.year.colon 2.912418e-10
## H.npnct02.log 2.666142e-10
## S.npnct22.log 2.461340e-10
## S.npnct03.log 2.304835e-10
## H.npnct05.log 1.846671e-10
## A.npnct07.log 1.653979e-10
## S.npnct02.log 7.575296e-11
## S.npnct08.log 7.353677e-11
## S.npnct09.log 6.715377e-11
## A.one.log 6.660788e-11
## S.one.log 6.580549e-11
## H.npnct11.log 6.416570e-11
## S.npnct15.log 5.392046e-12
## A.npnct15.log 4.905261e-12
## A.npnct18.log 1.783598e-12
## H.npnct22.log 1.467473e-12
## A.npnct19.log 0.000000e+00
## A.articl.log NA
## A.can.log NA
## A.compani.log NA
## A.day.log NA
## A.fashion.log NA
## A.first.log NA
## A.has.http NA
## A.intern.log NA
## A.make.log NA
## A.ndgts.log NA
## A.new.log NA
## A.newyork.log NA
## A.npnct01.log NA
## A.npnct02.log NA
## A.npnct03.log NA
## A.npnct04.log NA
## A.npnct05.log NA
## A.npnct06.log NA
## A.npnct08.log NA
## A.npnct09.log NA
## A.npnct10.log NA
## A.npnct11.log NA
## A.npnct12.log NA
## A.npnct13.log NA
## A.npnct16.log NA
## A.npnct20.log NA
## A.npnct22.log NA
## A.npnct24.log NA
## A.npnct25.log NA
## A.npnct26.log NA
## A.npnct28.log NA
## A.npnct29.log NA
## A.npnct30.log NA
## A.npnct31.log NA
## A.npnct32.log NA
## A.nuppr.log NA
## A.presid.log NA
## A.report.log NA
## A.share.log NA
## A.show.log NA
## A.take.log NA
## A.time.log NA
## A.week.log NA
## A.will.log NA
## A.year.log NA
## H.fashion.log NA
## H.has.http NA
## H.has.year.colon NA
## H.npnct03.log NA
## H.npnct06.log NA
## H.npnct08.log NA
## H.npnct10.log NA
## H.npnct18.log NA
## H.npnct19.log NA
## H.npnct20.log NA
## H.npnct23.log NA
## H.npnct24.log NA
## H.npnct25.log NA
## H.npnct26.log NA
## H.npnct27.log NA
## H.npnct28.log NA
## H.npnct29.log NA
## H.npnct31.log NA
## H.npnct32.log NA
## H.nwrds.unq.log NA
## H.X2015.log NA
## Popular NA
## Popular.fctr NA
## PubDate.last1 NA
## PubDate.last10 NA
## PubDate.last100 NA
## PubDate.month.fctr NA
## PubDate.POSIX NA
## PubDate.year.fctr NA
## PubDate.zoo NA
## S.has.http NA
## S.has.year.colon NA
## S.nchrs.log NA
## S.npnct05.log NA
## S.npnct07.log NA
## S.npnct10.log NA
## S.npnct11.log NA
## S.npnct14.log NA
## S.npnct17.log NA
## S.npnct18.log NA
## S.npnct19.log NA
## S.npnct20.log NA
## S.npnct21.log NA
## S.npnct23.log NA
## S.npnct24.log NA
## S.npnct25.log NA
## S.npnct26.log NA
## S.npnct27.log NA
## S.npnct28.log NA
## S.npnct29.log NA
## S.npnct31.log NA
## S.npnct32.log NA
## S.nwrds.log NA
## S.nwrds.unq.log NA
## S.said.log NA
## UniqueID NA
## WordCount NAglb_analytics_diag_plots(obs_df=glb_trnent_df, mdl_id=glb_fin_mdl_id,
prob_threshold=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_analytics_diag_plots(obs_df = glb_trnent_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 92
## [1] "Min/Max Boundaries: "
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## 116 116 Y 0.2051620389
## 33 33 Y 0.5092652390
## 1132 1132 Y 0.9390023962
## 1507 1507 N 0.0004584335
## 6370 6370 Y 0.6745455614
## 7 7 N 0.3098352282
## 20 20 N 0.7530306415
## 17 17 N 0.9014839570
## 15 15 N 0.9137842861
## 26 26 N 1.0000000000
## Popular.fctr.predict.Final.glm
## 116 N
## 33 Y
## 1132 Y
## 1507 N
## 6370 Y
## 7 Y
## 20 Y
## 17 Y
## 15 Y
## 26 Y
## Popular.fctr.predict.Final.glm.accurate
## 116 FALSE
## 33 TRUE
## 1132 TRUE
## 1507 TRUE
## 6370 TRUE
## 7 FALSE
## 20 FALSE
## 17 FALSE
## 15 FALSE
## 26 FALSE
## Popular.fctr.predict.Final.glm.error .label
## 116 -0.094837961 116
## 33 0.000000000 33
## 1132 0.000000000 1132
## 1507 0.000000000 1507
## 6370 0.000000000 6370
## 7 0.009835228 7
## 20 0.453030642 20
## 17 0.601483957 17
## 15 0.613784286 15
## 26 0.700000000 26
## [1] "Inaccurate: "
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## 2182 2182 Y 0.002532344
## 6101 6101 Y 0.002794767
## 1923 1923 Y 0.002901106
## 4721 4721 Y 0.002992607
## 3113 3113 Y 0.005772207
## 6441 6441 Y 0.006472160
## Popular.fctr.predict.Final.glm
## 2182 N
## 6101 N
## 1923 N
## 4721 N
## 3113 N
## 6441 N
## Popular.fctr.predict.Final.glm.accurate
## 2182 FALSE
## 6101 FALSE
## 1923 FALSE
## 4721 FALSE
## 3113 FALSE
## 6441 FALSE
## Popular.fctr.predict.Final.glm.error
## 2182 -0.2974677
## 6101 -0.2972052
## 1923 -0.2970989
## 4721 -0.2970074
## 3113 -0.2942278
## 6441 -0.2935278
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## 3113 3113 Y 0.005772207
## 3216 3216 Y 0.156023210
## 2318 2318 Y 0.197496539
## 3485 3485 Y 0.259227885
## 4987 4987 N 0.474808469
## 2216 2216 N 1.000000000
## Popular.fctr.predict.Final.glm
## 3113 N
## 3216 N
## 2318 N
## 3485 N
## 4987 Y
## 2216 Y
## Popular.fctr.predict.Final.glm.accurate
## 3113 FALSE
## 3216 FALSE
## 2318 FALSE
## 3485 FALSE
## 4987 FALSE
## 2216 FALSE
## Popular.fctr.predict.Final.glm.error
## 3113 -0.29422779
## 3216 -0.14397679
## 2318 -0.10250346
## 3485 -0.04077211
## 4987 0.17480847
## 2216 0.70000000
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## 6428 6428 N 1
## 6426 6426 N 1
## 6472 6472 N 1
## 6500 6500 N 1
## 6514 6514 N 1
## 6530 6530 N 1
## Popular.fctr.predict.Final.glm
## 6428 Y
## 6426 Y
## 6472 Y
## 6500 Y
## 6514 Y
## 6530 Y
## Popular.fctr.predict.Final.glm.accurate
## 6428 FALSE
## 6426 FALSE
## 6472 FALSE
## 6500 FALSE
## 6514 FALSE
## 6530 FALSE
## Popular.fctr.predict.Final.glm.error
## 6428 0.7
## 6426 0.7
## 6472 0.7
## 6500 0.7
## 6514 0.7
## 6530 0.7
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
print(glb_trnent_df[glb_trnent_df$UniqueID %in% FN_OOB_ids,
grep(glb_rsp_var, names(glb_trnent_df), value=TRUE)])## Popular.fctr Popular.fctr.predict.Final.glm.prob
## 92 Y 0.032201582
## 693 Y 0.059293075
## 4020 Y 0.007688928
## 4721 Y 0.002992607
## Popular.fctr.predict.Final.glm
## 92 N
## 693 N
## 4020 N
## 4721 Nsav_entity_df <- glb_entity_df
print(setdiff(names(glb_trnent_df), names(glb_entity_df)))## [1] "Popular.fctr.predict.Final.glm.prob"
## [2] "Popular.fctr.predict.Final.glm"for (col in setdiff(names(glb_trnent_df), names(glb_entity_df)))
# Merge or cbind ?
glb_entity_df[glb_entity_df$.src == "Train", col] <- glb_trnent_df[, col]
print(setdiff(names(glb_fitent_df), names(glb_entity_df)))## character(0)print(setdiff(names(glb_OOBent_df), names(glb_entity_df)))## character(0)for (col in setdiff(names(glb_OOBent_df), names(glb_entity_df)))
# Merge or cbind ?
glb_entity_df[glb_entity_df$.lcn == "OOB", col] <- glb_OOBent_df[, col]
print(setdiff(names(glb_newent_df), names(glb_entity_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_entity_df,
#glb_trnent_df, glb_fitent_df, glb_OOBent_df, glb_newent_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 7 1 594.083 604.89 10.807
## 15 predict.data.new 8 0 604.891 NA NAStep 8.0: predict data new
# Compute final model predictions
glb_newent_df <- glb_get_predictions(glb_newent_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(glb_newent_df, mdl_id = glb_fin_mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.3glb_analytics_diag_plots(obs_df=glb_newent_df, mdl_id=glb_fin_mdl_id,
prob_threshold=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_analytics_diag_plots(obs_df = glb_newent_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 92## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 1870 rows containing missing values (geom_point).## Warning: Removed 1870 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 1870 rows containing missing values (geom_point).## Warning: Removed 1870 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 1870 rows containing missing values (geom_point).## Warning: Removed 1870 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 1870 rows containing missing values (geom_point).## Warning: Removed 1870 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 1870 rows containing missing values (geom_point).## Warning: Removed 1870 rows containing missing values (geom_point).
## [1] "Min/Max Boundaries: "
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## 6630 6630 <NA> 8.941092e-11
## 6753 6753 <NA> 7.883965e-01
## 7309 7309 <NA> 2.612492e-04
## Popular.fctr.predict.Final.glm
## 6630 N
## 6753 Y
## 7309 N
## Popular.fctr.predict.Final.glm.accurate
## 6630 NA
## 6753 NA
## 7309 NA
## Popular.fctr.predict.Final.glm.error .label
## 6630 0 6630
## 6753 0 6753
## 7309 0 7309
## [1] "Inaccurate: "
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## NA NA <NA> NA
## NA.1 NA <NA> NA
## NA.2 NA <NA> NA
## NA.3 NA <NA> NA
## NA.4 NA <NA> NA
## NA.5 NA <NA> NA
## Popular.fctr.predict.Final.glm
## NA <NA>
## NA.1 <NA>
## NA.2 <NA>
## NA.3 <NA>
## NA.4 <NA>
## NA.5 <NA>
## Popular.fctr.predict.Final.glm.accurate
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## Popular.fctr.predict.Final.glm.error
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## NA.258 NA <NA> NA
## NA.373 NA <NA> NA
## NA.568 NA <NA> NA
## NA.1724 NA <NA> NA
## NA.1759 NA <NA> NA
## NA.1843 NA <NA> NA
## Popular.fctr.predict.Final.glm
## NA.258 <NA>
## NA.373 <NA>
## NA.568 <NA>
## NA.1724 <NA>
## NA.1759 <NA>
## NA.1843 <NA>
## Popular.fctr.predict.Final.glm.accurate
## NA.258 NA
## NA.373 NA
## NA.568 NA
## NA.1724 NA
## NA.1759 NA
## NA.1843 NA
## Popular.fctr.predict.Final.glm.error
## NA.258 NA
## NA.373 NA
## NA.568 NA
## NA.1724 NA
## NA.1759 NA
## NA.1843 NA
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## NA.1864 NA <NA> NA
## NA.1865 NA <NA> NA
## NA.1866 NA <NA> NA
## NA.1867 NA <NA> NA
## NA.1868 NA <NA> NA
## NA.1869 NA <NA> NA
## Popular.fctr.predict.Final.glm
## NA.1864 <NA>
## NA.1865 <NA>
## NA.1866 <NA>
## NA.1867 <NA>
## NA.1868 <NA>
## NA.1869 <NA>
## Popular.fctr.predict.Final.glm.accurate
## NA.1864 NA
## NA.1865 NA
## NA.1866 NA
## NA.1867 NA
## NA.1868 NA
## NA.1869 NA
## Popular.fctr.predict.Final.glm.error
## NA.1864 NA
## NA.1865 NA
## NA.1866 NA
## NA.1867 NA
## NA.1868 NA
## NA.1869 NA## Warning: Removed 1870 rows containing missing values (geom_point).
submit_df <- glb_newent_df[, c(glb_id_vars,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
write.csv(submit_df,
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv"), row.names=FALSE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])## [1] 0.3print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: Low.cor.X.glm"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.glm"print(dim(glb_fitent_df))## [1] 4475 204print(dsp_models_df)## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 8 Low.cor.X.glm 0.9027710 0.9226367 0.6664245
## 9 All.X.glm 0.8930481 0.8196249 0.6247359
## 10 All.X.no.rnorm.rpart 0.8862421 0.7084504 0.5054039
## 11 All.X.no.rnorm.rf 0.8847837 0.9189958 0.6111869
## 1 MFO.myMFO_classfr 0.8327662 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.8327662 0.5000000 0.0000000
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.8327662 0.5000000 0.0000000
## 5 Max.cor.Y.rpart 0.8327662 0.5000000 0.0000000
## 7 Interact.High.cor.Y.glm 0.7953330 0.7766863 0.3354449
## 6 Max.cor.Y.glm 0.7316480 0.7102060 0.2283681
## 2 Random.myrandom_classfr 0.1672338 0.4909227 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 8 2092.942 0.3
## 9 33948.772 0.9
## 10 NA 0.7
## 11 NA 0.3
## 1 NA 0.5
## 3 NA 0.5
## 4 NA 0.5
## 5 NA 0.5
## 7 3419.307 0.3
## 6 3714.601 0.2
## 2 NA 0.1print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))## [1] "Low.cor.X.glm OOB confusion matrix & accuracy: "print(t(confusionMatrix(glb_OOBent_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBent_df[, glb_rsp_var])$table))## Prediction
## Reference N Y
## N 1593 120
## Y 80 264tmp_OOBent_df <- glb_OOBent_df[, c("myCategory", predct_accurate_var_name)]
names(tmp_OOBent_df)[2] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBent_df, names(tmp_OOBent_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))## [1] "myCategory" ".n.OOB"glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))## myCategory .n.OOB .n.Tst .freqRatio.Tst
## 1 ## 407 338 0.180748663
## 6 Business#Business Day#Dealbook 312 304 0.162566845
## 15 OpEd#Opinion# 154 164 0.087700535
## 18 Styles#U.S.# 54 62 0.033155080
## 9 Business#Technology# 114 113 0.060427807
## 16 Science#Health# 66 57 0.030481283
## 10 Culture#Arts# 225 244 0.130481283
## 8 Business#Crosswords/Games# 40 42 0.022459893
## 13 Metro#N.Y. / Region# 60 67 0.035828877
## 3 #Opinion#Room For Debate 21 24 0.012834225
## 4 #Opinion#The Public Editor 10 10 0.005347594
## 7 Business#Business Day#Small Business 45 42 0.022459893
## 20 TStyle## 221 105 0.056149733
## 17 Styles##Fashion 41 15 0.008021390
## 2 #Multimedia# 42 52 0.027807487
## 5 #U.S.#Education 93 90 0.048128342
## 11 Foreign#World# 47 47 0.025133690
## 12 Foreign#World#Asia Pacific 61 56 0.029946524
## 14 myOther 13 3 0.001604278
## 19 Travel#Travel# 31 35 0.018716578
## .freqRatio.OOB accurate.OOB.FALSE accurate.OOB.TRUE max.accuracy.OOB
## 1 0.197860963 38 369 0.9066339
## 6 0.151677200 33 279 0.8942308
## 15 0.074866310 27 127 0.8246753
## 18 0.026251823 25 29 0.5370370
## 9 0.055420515 21 93 0.8157895
## 16 0.032085561 20 46 0.6969697
## 10 0.109382596 11 214 0.9511111
## 8 0.019445795 8 32 0.8000000
## 13 0.029168692 6 54 0.9000000
## 3 0.010209042 3 18 0.8571429
## 4 0.004861449 3 7 0.7000000
## 7 0.021876519 2 43 0.9555556
## 20 0.107438017 2 219 0.9909502
## 17 0.019931940 1 40 0.9756098
## 2 0.020418085 0 42 1.0000000
## 5 0.045211473 0 93 1.0000000
## 11 0.022848809 0 47 1.0000000
## 12 0.029654837 0 61 1.0000000
## 14 0.006319883 0 13 1.0000000
## 19 0.015070491 0 31 1.0000000dsp_NewsDesk.nb_conf_mtrx <- function(NewsDesk.nb) {
print(sprintf("%s OOB::NewsDesk.nb=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, NewsDesk.nb))
print(t(confusionMatrix(
glb_OOBent_df[glb_OOBent_df$NewsDesk.nb == NewsDesk.nb,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBent_df[glb_OOBent_df$NewsDesk.nb == NewsDesk.nb, glb_rsp_var])$table))
print(sum(glb_OOBent_df[glb_OOBent_df$NewsDesk.nb == NewsDesk.nb,
predct_accurate_var_name]) /
nrow(glb_OOBent_df[glb_OOBent_df$NewsDesk.nb == NewsDesk.nb, ]))
err_ids <- glb_OOBent_df[(glb_OOBent_df$NewsDesk.nb == NewsDesk.nb) &
(!glb_OOBent_df[, predct_accurate_var_name]), glb_id_vars]
print(sprintf("%s OOB::NewsDesk.nb=%s errors: ", glb_sel_mdl_id, NewsDesk.nb))
print(glb_OOBent_df[glb_OOBent_df$UniqueID %in% err_ids,
c("Headline.pfx", "Headline", "Popular")])
}
#dsp_NewsDesk.nb_conf_mtrx(NewsDesk.nb="myMultimedia")
print("FN_OOB_ids:")## [1] "FN_OOB_ids:"print(glb_OOBent_df[glb_OOBent_df$UniqueID %in% FN_OOB_ids,
grep(glb_rsp_var, names(glb_OOBent_df), value=TRUE)])## [1] Popular.fctr
## [2] Popular.fctr.predict.Low.cor.X.glm.prob
## [3] Popular.fctr.predict.Low.cor.X.glm
## [4] Popular.fctr.predict.Low.cor.X.glm.accurate
## <0 rows> (or 0-length row.names)print(glb_OOBent_df[glb_OOBent_df$UniqueID %in% FN_OOB_ids,
glb_txt_vars])## [1] Headline Snippet Abstract
## <0 rows> (or 0-length row.names)print(dsp_vctr <- colSums(glb_OOBent_df[glb_OOBent_df$UniqueID %in% FN_OOB_ids,
setdiff(grep("[HSA].", names(glb_OOBent_df), value=TRUE),
union(myfind_chr_cols_df(glb_OOBent_df),
grep(".fctr", names(glb_OOBent_df), fixed=TRUE, value=TRUE)))]))## PubDate.POSIX H.X2014.log H.X2015.log H.daili.log
## 0 0 0 0
## H.day.log H.fashion.log H.new.log H.newyork.log
## 0 0 0 0
## H.report.log H.today.log H.week.log H.has.http
## 0 0 0 0
## H.has.ebola H.nwrds.log H.nwrds.unq.log H.nchrs.log
## 0 0 0 0
## H.nuppr.log H.ndgts.log H.npnct01.log H.npnct02.log
## 0 0 0 0
## H.npnct03.log H.npnct04.log H.npnct05.log H.npnct06.log
## 0 0 0 0
## H.npnct07.log H.npnct08.log H.npnct09.log H.npnct10.log
## 0 0 0 0
## H.npnct11.log H.npnct12.log H.npnct13.log H.npnct14.log
## 0 0 0 0
## H.npnct15.log H.npnct16.log H.npnct17.log H.npnct18.log
## 0 0 0 0
## H.npnct19.log H.npnct20.log H.npnct21.log H.npnct22.log
## 0 0 0 0
## H.npnct23.log H.npnct24.log H.npnct25.log H.npnct26.log
## 0 0 0 0
## H.npnct27.log H.npnct28.log H.npnct29.log H.npnct30.log
## 0 0 0 0
## H.npnct31.log H.npnct32.log H.has.year.colon S.articl.log
## 0 0 0 0
## S.can.log S.compani.log S.day.log S.fashion.log
## 0 0 0 0
## S.first.log S.intern.log S.make.log S.new.log
## 0 0 0 0
## S.newyork.log S.one.log S.presid.log S.report.log
## 0 0 0 0
## S.said.log S.share.log S.show.log S.state.log
## 0 0 0 0
## S.take.log S.time.log S.week.log S.will.log
## 0 0 0 0
## S.year.log S.has.http S.nwrds.log S.nwrds.unq.log
## 0 0 0 0
## S.nchrs.log S.nuppr.log S.ndgts.log S.npnct01.log
## 0 0 0 0
## S.npnct02.log S.npnct03.log S.npnct04.log S.npnct05.log
## 0 0 0 0
## S.npnct06.log S.npnct07.log S.npnct08.log S.npnct09.log
## 0 0 0 0
## S.npnct10.log S.npnct11.log S.npnct12.log S.npnct13.log
## 0 0 0 0
## S.npnct14.log S.npnct15.log S.npnct16.log S.npnct17.log
## 0 0 0 0
## S.npnct18.log S.npnct19.log S.npnct20.log S.npnct21.log
## 0 0 0 0
## S.npnct22.log S.npnct23.log S.npnct24.log S.npnct25.log
## 0 0 0 0
## S.npnct26.log S.npnct27.log S.npnct28.log S.npnct29.log
## 0 0 0 0
## S.npnct30.log S.npnct31.log S.npnct32.log S.has.year.colon
## 0 0 0 0
## A.articl.log A.can.log A.compani.log A.day.log
## 0 0 0 0
## A.fashion.log A.first.log A.intern.log A.make.log
## 0 0 0 0
## A.new.log A.newyork.log A.one.log A.presid.log
## 0 0 0 0
## A.report.log A.said.log A.share.log A.show.log
## 0 0 0 0
## A.state.log A.take.log A.time.log A.week.log
## 0 0 0 0
## A.will.log A.year.log A.has.http A.nwrds.log
## 0 0 0 0
## A.nwrds.unq.log A.nchrs.log A.nuppr.log A.ndgts.log
## 0 0 0 0
## A.npnct01.log A.npnct02.log A.npnct03.log A.npnct04.log
## 0 0 0 0
## A.npnct05.log A.npnct06.log A.npnct07.log A.npnct08.log
## 0 0 0 0
## A.npnct09.log A.npnct10.log A.npnct11.log A.npnct12.log
## 0 0 0 0
## A.npnct13.log A.npnct14.log A.npnct15.log A.npnct16.log
## 0 0 0 0
## A.npnct17.log A.npnct18.log A.npnct19.log A.npnct20.log
## 0 0 0 0
## A.npnct21.log A.npnct22.log A.npnct23.log A.npnct24.log
## 0 0 0 0
## A.npnct25.log A.npnct26.log A.npnct27.log A.npnct28.log
## 0 0 0 0
## A.npnct29.log A.npnct30.log A.npnct31.log A.npnct32.log
## 0 0 0 0
## A.has.year.colon
## 0dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBent_df[glb_OOBent_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBent_df), value=TRUE)])
print(glb_newent_df[glb_newent_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newent_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newent_df[glb_newent_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newent_df), value=TRUE),
union(myfind_chr_cols_df(glb_newent_df),
grep(".fctr", names(glb_newent_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newent_df[glb_newent_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newent_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBent_df[glb_OOBent_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBent_df), value=TRUE)])
# print(glb_OOBent_df[glb_OOBent_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_entity_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_entity_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_entity_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(subset(glb_feats_df, !is.na(importance))[,
c("zeroVar", "nzv",
grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])## zeroVar nzv importance Low.cor.X.glm.importance
## A.npnct23.log FALSE TRUE 1.000000e+02 1.000000e+02
## WordCount.log FALSE FALSE 1.288801e-05 1.288801e-05
## myCategory.fctr FALSE FALSE 1.002204e-05 1.002204e-05
## H.npnct21.log FALSE FALSE 5.018143e-06 5.018143e-06
## A.npnct21.log FALSE FALSE 4.625851e-06 4.625851e-06
## S.nuppr.log FALSE FALSE 4.569486e-06 4.569486e-06
## A.npnct14.log FALSE FALSE 3.906452e-06 3.906452e-06
## H.today.log FALSE TRUE 3.716137e-06 3.716137e-06
## H.nuppr.log FALSE FALSE 3.182025e-06 3.182025e-06
## H.npnct09.log FALSE TRUE 3.059125e-06 3.059125e-06
## PubDate.wkday.fctr FALSE FALSE 2.985990e-06 2.985990e-06
## S.ndgts.log FALSE FALSE 2.333324e-06 2.333324e-06
## H.nchrs.log FALSE FALSE 2.327769e-06 2.327769e-06
## S.report.log FALSE TRUE 2.241758e-06 2.241758e-06
## A.said.log FALSE TRUE 2.225691e-06 2.225691e-06
## S.newyork.log FALSE FALSE 2.215953e-06 2.215953e-06
## H.npnct12.log FALSE FALSE 2.149261e-06 2.149261e-06
## H.npnct04.log FALSE TRUE 2.100497e-06 2.100497e-06
## PubDate.last10.log FALSE FALSE 1.997887e-06 1.997887e-06
## H.ndgts.log FALSE FALSE 1.993583e-06 1.993583e-06
## H.nwrds.log FALSE FALSE 1.855196e-06 1.855196e-06
## H.npnct17.log FALSE TRUE 1.847313e-06 1.847313e-06
## S.npnct04.log FALSE TRUE 1.760068e-06 1.760068e-06
## PubDate.second.fctr FALSE FALSE 1.752346e-06 1.752346e-06
## S.can.log FALSE TRUE 1.698109e-06 1.698109e-06
## S.share.log FALSE TRUE 1.577347e-06 1.577347e-06
## H.new.log FALSE TRUE 1.501228e-06 1.501228e-06
## S.will.log FALSE FALSE 1.438120e-06 1.438120e-06
## H.npnct07.log FALSE FALSE 1.251748e-06 1.251748e-06
## S.take.log FALSE TRUE 1.242853e-06 1.242853e-06
## PubDate.minute.fctr FALSE FALSE 1.202598e-06 1.202598e-06
## PubDate.last1.log FALSE FALSE 1.200389e-06 1.200389e-06
## S.npnct01.log FALSE TRUE 1.175535e-06 1.175535e-06
## H.npnct13.log FALSE FALSE 1.166146e-06 1.166146e-06
## H.day.log FALSE TRUE 1.165449e-06 1.165449e-06
## H.npnct14.log FALSE TRUE 1.162212e-06 1.162212e-06
## S.npnct12.log FALSE FALSE 1.145878e-06 1.145878e-06
## S.compani.log FALSE FALSE 1.136156e-06 1.136156e-06
## H.npnct01.log FALSE TRUE 1.095863e-06 1.095863e-06
## A.nwrds.unq.log FALSE FALSE 1.074022e-06 1.074022e-06
## S.show.log FALSE TRUE 1.071063e-06 1.071063e-06
## PubDate.hour.fctr FALSE FALSE 1.052433e-06 1.052433e-06
## S.presid.log FALSE TRUE 1.031221e-06 1.031221e-06
## S.npnct16.log FALSE FALSE 1.022348e-06 1.022348e-06
## S.make.log FALSE TRUE 1.008816e-06 1.008816e-06
## S.state.log FALSE TRUE 9.110520e-07 9.110520e-07
## A.state.log FALSE TRUE 9.110520e-07 9.110520e-07
## A.npnct27.log FALSE TRUE 9.110520e-07 9.110520e-07
## S.year.log FALSE FALSE 8.851511e-07 8.851511e-07
## H.npnct16.log FALSE FALSE 8.710662e-07 8.710662e-07
## S.intern.log FALSE TRUE 8.387696e-07 8.387696e-07
## H.has.ebola FALSE TRUE 8.241580e-07 8.241580e-07
## PubDate.date.fctr FALSE FALSE 8.233073e-07 8.233073e-07
## H.report.log FALSE TRUE 8.039331e-07 8.039331e-07
## H.X2014.log FALSE TRUE 7.740347e-07 7.740347e-07
## S.npnct13.log FALSE FALSE 7.695263e-07 7.695263e-07
## H.week.log FALSE TRUE 7.110312e-07 7.110312e-07
## S.time.log FALSE FALSE 6.384092e-07 6.384092e-07
## PubDate.wkend FALSE FALSE 6.089193e-07 6.089193e-07
## S.week.log FALSE FALSE 5.723540e-07 5.723540e-07
## A.nwrds.log FALSE FALSE 5.580949e-07 5.580949e-07
## A.nchrs.log FALSE FALSE 5.287682e-07 5.287682e-07
## PubDate.last100.log FALSE FALSE 5.212444e-07 5.212444e-07
## S.day.log FALSE TRUE 3.145965e-07 3.145965e-07
## A.npnct17.log FALSE TRUE 3.000151e-07 3.000151e-07
## S.first.log FALSE TRUE 2.555518e-07 2.555518e-07
## H.newyork.log FALSE TRUE 1.800829e-07 1.800829e-07
## S.articl.log FALSE TRUE 1.796068e-07 1.796068e-07
## H.daili.log FALSE TRUE 1.287108e-07 1.287108e-07
## H.npnct30.log FALSE TRUE 9.686374e-08 9.686374e-08
## .rnorm FALSE FALSE 8.784761e-08 8.784761e-08
## S.new.log FALSE FALSE 4.862671e-08 4.862671e-08
## S.npnct06.log FALSE TRUE 2.225651e-08 2.225651e-08
## S.fashion.log FALSE TRUE 1.098049e-09 1.098049e-09
## H.npnct15.log FALSE TRUE 7.395235e-10 7.395235e-10
## S.npnct30.log FALSE TRUE 5.609428e-10 5.609428e-10
## A.has.year.colon FALSE TRUE 2.912418e-10 2.912418e-10
## H.npnct02.log FALSE TRUE 2.666142e-10 2.666142e-10
## S.npnct22.log FALSE TRUE 2.461340e-10 2.461340e-10
## S.npnct03.log FALSE TRUE 2.304835e-10 2.304835e-10
## H.npnct05.log FALSE TRUE 1.846671e-10 1.846671e-10
## A.npnct07.log FALSE TRUE 1.653979e-10 1.653979e-10
## S.npnct02.log FALSE TRUE 7.575296e-11 7.575296e-11
## S.npnct08.log FALSE TRUE 7.353677e-11 7.353677e-11
## S.npnct09.log FALSE TRUE 6.715377e-11 6.715377e-11
## A.one.log FALSE TRUE 6.660788e-11 6.660788e-11
## S.one.log FALSE TRUE 6.580549e-11 6.580549e-11
## H.npnct11.log FALSE TRUE 6.416570e-11 6.416570e-11
## S.npnct15.log FALSE TRUE 5.392046e-12 5.392046e-12
## A.npnct15.log FALSE TRUE 4.905261e-12 4.905261e-12
## A.npnct18.log FALSE TRUE 1.783598e-12 1.783598e-12
## H.npnct22.log FALSE TRUE 1.467473e-12 1.467473e-12
## A.npnct19.log FALSE TRUE 0.000000e+00 0.000000e+00
## Final.glm.importance
## A.npnct23.log 1.000000e+02
## WordCount.log 1.288801e-05
## myCategory.fctr 1.002204e-05
## H.npnct21.log 5.018143e-06
## A.npnct21.log 4.625851e-06
## S.nuppr.log 4.569486e-06
## A.npnct14.log 3.906452e-06
## H.today.log 3.716137e-06
## H.nuppr.log 3.182025e-06
## H.npnct09.log 3.059125e-06
## PubDate.wkday.fctr 2.985990e-06
## S.ndgts.log 2.333324e-06
## H.nchrs.log 2.327769e-06
## S.report.log 2.241758e-06
## A.said.log 2.225691e-06
## S.newyork.log 2.215953e-06
## H.npnct12.log 2.149261e-06
## H.npnct04.log 2.100497e-06
## PubDate.last10.log 1.997887e-06
## H.ndgts.log 1.993583e-06
## H.nwrds.log 1.855196e-06
## H.npnct17.log 1.847313e-06
## S.npnct04.log 1.760068e-06
## PubDate.second.fctr 1.752346e-06
## S.can.log 1.698109e-06
## S.share.log 1.577347e-06
## H.new.log 1.501228e-06
## S.will.log 1.438120e-06
## H.npnct07.log 1.251748e-06
## S.take.log 1.242853e-06
## PubDate.minute.fctr 1.202598e-06
## PubDate.last1.log 1.200389e-06
## S.npnct01.log 1.175535e-06
## H.npnct13.log 1.166146e-06
## H.day.log 1.165449e-06
## H.npnct14.log 1.162212e-06
## S.npnct12.log 1.145878e-06
## S.compani.log 1.136156e-06
## H.npnct01.log 1.095863e-06
## A.nwrds.unq.log 1.074022e-06
## S.show.log 1.071063e-06
## PubDate.hour.fctr 1.052433e-06
## S.presid.log 1.031221e-06
## S.npnct16.log 1.022348e-06
## S.make.log 1.008816e-06
## S.state.log 9.110520e-07
## A.state.log 9.110520e-07
## A.npnct27.log 9.110520e-07
## S.year.log 8.851511e-07
## H.npnct16.log 8.710662e-07
## S.intern.log 8.387696e-07
## H.has.ebola 8.241580e-07
## PubDate.date.fctr 8.233073e-07
## H.report.log 8.039331e-07
## H.X2014.log 7.740347e-07
## S.npnct13.log 7.695263e-07
## H.week.log 7.110312e-07
## S.time.log 6.384092e-07
## PubDate.wkend 6.089193e-07
## S.week.log 5.723540e-07
## A.nwrds.log 5.580949e-07
## A.nchrs.log 5.287682e-07
## PubDate.last100.log 5.212444e-07
## S.day.log 3.145965e-07
## A.npnct17.log 3.000151e-07
## S.first.log 2.555518e-07
## H.newyork.log 1.800829e-07
## S.articl.log 1.796068e-07
## H.daili.log 1.287108e-07
## H.npnct30.log 9.686374e-08
## .rnorm 8.784761e-08
## S.new.log 4.862671e-08
## S.npnct06.log 2.225651e-08
## S.fashion.log 1.098049e-09
## H.npnct15.log 7.395235e-10
## S.npnct30.log 5.609428e-10
## A.has.year.colon 2.912418e-10
## H.npnct02.log 2.666142e-10
## S.npnct22.log 2.461340e-10
## S.npnct03.log 2.304835e-10
## H.npnct05.log 1.846671e-10
## A.npnct07.log 1.653979e-10
## S.npnct02.log 7.575296e-11
## S.npnct08.log 7.353677e-11
## S.npnct09.log 6.715377e-11
## A.one.log 6.660788e-11
## S.one.log 6.580549e-11
## H.npnct11.log 6.416570e-11
## S.npnct15.log 5.392046e-12
## A.npnct15.log 4.905261e-12
## A.npnct18.log 1.783598e-12
## H.npnct22.log 1.467473e-12
## A.npnct19.log 0.000000e+00print(subset(glb_feats_df, is.na(importance))[,
c("zeroVar", "nzv",
grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])## zeroVar nzv importance Low.cor.X.glm.importance
## A.articl.log FALSE TRUE NA NA
## A.can.log FALSE TRUE NA NA
## A.compani.log FALSE FALSE NA NA
## A.day.log FALSE TRUE NA NA
## A.fashion.log FALSE TRUE NA NA
## A.first.log FALSE TRUE NA NA
## A.has.http FALSE TRUE NA NA
## A.intern.log FALSE TRUE NA NA
## A.make.log FALSE TRUE NA NA
## A.ndgts.log FALSE FALSE NA NA
## A.new.log FALSE FALSE NA NA
## A.newyork.log FALSE FALSE NA NA
## A.npnct01.log FALSE TRUE NA NA
## A.npnct02.log FALSE TRUE NA NA
## A.npnct03.log FALSE TRUE NA NA
## A.npnct04.log FALSE TRUE NA NA
## A.npnct05.log TRUE TRUE NA NA
## A.npnct06.log FALSE TRUE NA NA
## A.npnct08.log FALSE TRUE NA NA
## A.npnct09.log FALSE TRUE NA NA
## A.npnct10.log TRUE TRUE NA NA
## A.npnct11.log FALSE TRUE NA NA
## A.npnct12.log FALSE FALSE NA NA
## A.npnct13.log FALSE FALSE NA NA
## A.npnct16.log FALSE FALSE NA NA
## A.npnct20.log FALSE TRUE NA NA
## A.npnct22.log FALSE TRUE NA NA
## A.npnct24.log TRUE TRUE NA NA
## A.npnct25.log FALSE TRUE NA NA
## A.npnct26.log TRUE TRUE NA NA
## A.npnct28.log TRUE TRUE NA NA
## A.npnct29.log TRUE TRUE NA NA
## A.npnct30.log FALSE TRUE NA NA
## A.npnct31.log TRUE TRUE NA NA
## A.npnct32.log TRUE TRUE NA NA
## A.nuppr.log FALSE FALSE NA NA
## A.presid.log FALSE TRUE NA NA
## A.report.log FALSE TRUE NA NA
## A.share.log FALSE TRUE NA NA
## A.show.log FALSE TRUE NA NA
## A.take.log FALSE TRUE NA NA
## A.time.log FALSE FALSE NA NA
## A.week.log FALSE FALSE NA NA
## A.will.log FALSE FALSE NA NA
## A.year.log FALSE FALSE NA NA
## H.fashion.log FALSE TRUE NA NA
## H.has.http TRUE TRUE NA NA
## H.has.year.colon FALSE TRUE NA NA
## H.npnct03.log FALSE TRUE NA NA
## H.npnct06.log FALSE TRUE NA NA
## H.npnct08.log FALSE TRUE NA NA
## H.npnct10.log TRUE TRUE NA NA
## H.npnct18.log TRUE TRUE NA NA
## H.npnct19.log TRUE TRUE NA NA
## H.npnct20.log TRUE TRUE NA NA
## H.npnct23.log TRUE TRUE NA NA
## H.npnct24.log TRUE TRUE NA NA
## H.npnct25.log TRUE TRUE NA NA
## H.npnct26.log TRUE TRUE NA NA
## H.npnct27.log TRUE TRUE NA NA
## H.npnct28.log TRUE TRUE NA NA
## H.npnct29.log TRUE TRUE NA NA
## H.npnct31.log TRUE TRUE NA NA
## H.npnct32.log TRUE TRUE NA NA
## H.nwrds.unq.log FALSE FALSE NA NA
## H.X2015.log FALSE TRUE NA NA
## Popular FALSE FALSE NA NA
## Popular.fctr NA NA NA NA
## PubDate.last1 FALSE FALSE NA NA
## PubDate.last10 FALSE FALSE NA NA
## PubDate.last100 FALSE FALSE NA NA
## PubDate.month.fctr FALSE FALSE NA NA
## PubDate.POSIX FALSE FALSE NA NA
## PubDate.year.fctr TRUE TRUE NA NA
## PubDate.zoo FALSE FALSE NA NA
## S.has.http TRUE TRUE NA NA
## S.has.year.colon FALSE TRUE NA NA
## S.nchrs.log FALSE FALSE NA NA
## S.npnct05.log TRUE TRUE NA NA
## S.npnct07.log FALSE TRUE NA NA
## S.npnct10.log TRUE TRUE NA NA
## S.npnct11.log FALSE TRUE NA NA
## S.npnct14.log FALSE FALSE NA NA
## S.npnct17.log FALSE TRUE NA NA
## S.npnct18.log TRUE TRUE NA NA
## S.npnct19.log TRUE TRUE NA NA
## S.npnct20.log TRUE TRUE NA NA
## S.npnct21.log FALSE FALSE NA NA
## S.npnct23.log FALSE TRUE NA NA
## S.npnct24.log TRUE TRUE NA NA
## S.npnct25.log FALSE TRUE NA NA
## S.npnct26.log TRUE TRUE NA NA
## S.npnct27.log TRUE TRUE NA NA
## S.npnct28.log TRUE TRUE NA NA
## S.npnct29.log TRUE TRUE NA NA
## S.npnct31.log TRUE TRUE NA NA
## S.npnct32.log TRUE TRUE NA NA
## S.nwrds.log FALSE FALSE NA NA
## S.nwrds.unq.log FALSE FALSE NA NA
## S.said.log FALSE TRUE NA NA
## UniqueID FALSE FALSE NA NA
## WordCount FALSE FALSE NA NA
## Final.glm.importance
## A.articl.log NA
## A.can.log NA
## A.compani.log NA
## A.day.log NA
## A.fashion.log NA
## A.first.log NA
## A.has.http NA
## A.intern.log NA
## A.make.log NA
## A.ndgts.log NA
## A.new.log NA
## A.newyork.log NA
## A.npnct01.log NA
## A.npnct02.log NA
## A.npnct03.log NA
## A.npnct04.log NA
## A.npnct05.log NA
## A.npnct06.log NA
## A.npnct08.log NA
## A.npnct09.log NA
## A.npnct10.log NA
## A.npnct11.log NA
## A.npnct12.log NA
## A.npnct13.log NA
## A.npnct16.log NA
## A.npnct20.log NA
## A.npnct22.log NA
## A.npnct24.log NA
## A.npnct25.log NA
## A.npnct26.log NA
## A.npnct28.log NA
## A.npnct29.log NA
## A.npnct30.log NA
## A.npnct31.log NA
## A.npnct32.log NA
## A.nuppr.log NA
## A.presid.log NA
## A.report.log NA
## A.share.log NA
## A.show.log NA
## A.take.log NA
## A.time.log NA
## A.week.log NA
## A.will.log NA
## A.year.log NA
## H.fashion.log NA
## H.has.http NA
## H.has.year.colon NA
## H.npnct03.log NA
## H.npnct06.log NA
## H.npnct08.log NA
## H.npnct10.log NA
## H.npnct18.log NA
## H.npnct19.log NA
## H.npnct20.log NA
## H.npnct23.log NA
## H.npnct24.log NA
## H.npnct25.log NA
## H.npnct26.log NA
## H.npnct27.log NA
## H.npnct28.log NA
## H.npnct29.log NA
## H.npnct31.log NA
## H.npnct32.log NA
## H.nwrds.unq.log NA
## H.X2015.log NA
## Popular NA
## Popular.fctr NA
## PubDate.last1 NA
## PubDate.last10 NA
## PubDate.last100 NA
## PubDate.month.fctr NA
## PubDate.POSIX NA
## PubDate.year.fctr NA
## PubDate.zoo NA
## S.has.http NA
## S.has.year.colon NA
## S.nchrs.log NA
## S.npnct05.log NA
## S.npnct07.log NA
## S.npnct10.log NA
## S.npnct11.log NA
## S.npnct14.log NA
## S.npnct17.log NA
## S.npnct18.log NA
## S.npnct19.log NA
## S.npnct20.log NA
## S.npnct21.log NA
## S.npnct23.log NA
## S.npnct24.log NA
## S.npnct25.log NA
## S.npnct26.log NA
## S.npnct27.log NA
## S.npnct28.log NA
## S.npnct29.log NA
## S.npnct31.log NA
## S.npnct32.log NA
## S.nwrds.log NA
## S.nwrds.unq.log NA
## S.said.log NA
## UniqueID NA
## WordCount NAsav_entity_df <- glb_entity_df
print(setdiff(names(glb_trnent_df), names(glb_entity_df)))## character(0)for (col in setdiff(names(glb_trnent_df), names(glb_entity_df)))
# Merge or cbind ?
glb_entity_df[glb_entity_df$.src == "Train", col] <- glb_trnent_df[, col]
print(setdiff(names(glb_fitent_df), names(glb_entity_df)))## character(0)print(setdiff(names(glb_OOBent_df), names(glb_entity_df)))## character(0)for (col in setdiff(names(glb_OOBent_df), names(glb_entity_df)))
# Merge or cbind ?
glb_entity_df[glb_entity_df$.lcn == "OOB", col] <- glb_OOBent_df[, col]
print(setdiff(names(glb_newent_df), names(glb_entity_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_entity_df,
#glb_trnent_df, glb_fitent_df, glb_OOBent_df, glb_newent_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 predict.data.new 8 0 604.891 613.612 8.721
## 16 display.session.info 9 0 613.612 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 10 fit.models 6 1 222.291 544.291 322.000
## 6 extract.features 3 0 39.055 148.235 109.180
## 9 fit.models 6 0 176.477 222.290 45.814
## 7 select.features 4 0 148.235 175.347 27.112
## 13 fit.data.training 7 0 569.038 594.083 25.045
## 2 inspect.data 2 0 12.185 30.473 18.288
## 11 fit.models 6 2 544.291 561.516 17.226
## 14 fit.data.training 7 1 594.083 604.890 10.807
## 15 predict.data.new 8 0 604.891 613.612 8.721
## 12 fit.models 6 3 561.517 569.037 7.520
## 4 manage.missing.data 2 2 34.314 38.998 4.684
## 3 cleanse.data 2 1 30.474 34.314 3.840
## 8 partition.data.training 5 0 175.348 176.476 1.128
## 1 import.data 1 0 11.135 12.185 1.050
## 5 encode.data 2 3 38.998 39.054 0.056
## duration
## 10 322.000
## 6 109.180
## 9 45.813
## 7 27.112
## 13 25.045
## 2 18.288
## 11 17.225
## 14 10.807
## 15 8.721
## 12 7.520
## 4 4.684
## 3 3.840
## 8 1.128
## 1 1.050
## 5 0.056## [1] "Total Elapsed Time: 613.612 secs"
## R version 3.1.3 (2015-03-09)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.3 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] randomForest_4.6-10 rpart.plot_1.5.2 rpart_4.1-9
## [4] ROCR_1.0-7 gplots_2.16.0 caTools_1.17.1
## [7] caret_6.0-41 tm_0.6 NLP_0.1-6
## [10] mice_2.22 lattice_0.20-31 Rcpp_0.11.5
## [13] plyr_1.8.1 zoo_1.7-12 sqldf_0.4-10
## [16] RSQLite_1.0.0 DBI_0.3.1 gsubfn_0.6-6
## [19] proto_0.3-10 reshape2_1.4.1 doBy_4.5-13
## [22] survival_2.38-1 ggplot2_1.0.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 BradleyTerry2_1.0-6 brglm_0.5-9
## [4] car_2.0-25 chron_2.3-45 class_7.3-12
## [7] codetools_0.2-11 colorspace_1.2-6 compiler_3.1.3
## [10] digest_0.6.8 e1071_1.6-4 evaluate_0.5.5
## [13] foreach_1.4.2 formatR_1.1 gdata_2.13.3
## [16] gtable_0.1.2 gtools_3.4.1 htmltools_0.2.6
## [19] iterators_1.0.7 KernSmooth_2.23-14 knitr_1.9
## [22] labeling_0.3 lme4_1.1-7 MASS_7.3-40
## [25] Matrix_1.2-0 mgcv_1.8-6 minqa_1.2.4
## [28] munsell_0.4.2 nlme_3.1-120 nloptr_1.0.4
## [31] nnet_7.3-9 parallel_3.1.3 pbkrtest_0.4-2
## [34] quantreg_5.11 RColorBrewer_1.1-2 rmarkdown_0.5.1
## [37] scales_0.2.4 slam_0.1-32 SparseM_1.6
## [40] splines_3.1.3 stringr_0.6.2 tools_3.1.3
## [43] yaml_2.1.13